The requirement of this project is to classify and detect cars in the image dataset provided.
# Initializing the random number generator
import random
random.seed(0)
# Ignoring the warnings
import warnings
warnings.filterwarnings("ignore")
# Mounting the google drive to load the data file
from google.colab import drive
drive.mount('/content/drive/')
import os
path = '/content/drive/My Drive/capstone/Dataset'
capstone_path = '/content/drive/My Drive/capstone'
os.chdir(path)
Mounted at /content/drive/
!pip uninstall opencv-python-headless
Found existing installation: opencv-python-headless 4.1.2.30
Uninstalling opencv-python-headless-4.1.2.30:
Would remove:
/usr/local/lib/python3.7/dist-packages/cv2/*
/usr/local/lib/python3.7/dist-packages/opencv_python_headless-4.1.2.30.dist-info/*
Would not remove (might be manually added):
/usr/local/lib/python3.7/dist-packages/cv2/.libs/libQtCore-bbdab771.so.4.8.7
/usr/local/lib/python3.7/dist-packages/cv2/.libs/libQtGui-903938cd.so.4.8.7
/usr/local/lib/python3.7/dist-packages/cv2/.libs/libQtTest-1183da5d.so.4.8.7
Proceed (y/n)? y
Successfully uninstalled opencv-python-headless-4.1.2.30
!pip install opencv-python-headless==4.1.2.30
Collecting opencv-python-headless==4.1.2.30 Using cached opencv_python_headless-4.1.2.30-cp37-cp37m-manylinux1_x86_64.whl (21.8 MB) Requirement already satisfied: numpy>=1.14.5 in /usr/local/lib/python3.7/dist-packages (from opencv-python-headless==4.1.2.30) (1.19.5) Installing collected packages: opencv-python-headless Successfully installed opencv-python-headless-4.1.2.30
!pip install imgaug
Requirement already satisfied: imgaug in /usr/local/lib/python3.7/dist-packages (0.2.9) Requirement already satisfied: Shapely in /usr/local/lib/python3.7/dist-packages (from imgaug) (1.8.0) Requirement already satisfied: imageio in /usr/local/lib/python3.7/dist-packages (from imgaug) (2.4.1) Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from imgaug) (4.1.2.30) Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from imgaug) (7.1.2) Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from imgaug) (1.19.5) Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from imgaug) (1.15.0) Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from imgaug) (3.2.2) Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from imgaug) (1.4.1) Requirement already satisfied: scikit-image>=0.11.0 in /usr/local/lib/python3.7/dist-packages (from imgaug) (0.18.3) Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.11.0->imgaug) (1.2.0) Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.11.0->imgaug) (2021.11.2) Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image>=0.11.0->imgaug) (2.6.3) Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug) (1.3.2) Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug) (3.0.7) Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug) (2.8.2) Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->imgaug) (0.11.0)
!pip install imagecorruptions
Collecting imagecorruptions
Downloading imagecorruptions-1.1.2-py3-none-any.whl (2.1 MB)
|████████████████████████████████| 2.1 MB 8.4 MB/s
Requirement already satisfied: Pillow>=5.4.1 in /usr/local/lib/python3.7/dist-packages (from imagecorruptions) (7.1.2)
Requirement already satisfied: opencv-python>=3.4.5 in /usr/local/lib/python3.7/dist-packages (from imagecorruptions) (4.1.2.30)
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Requirement already satisfied: numpy>=1.16 in /usr/local/lib/python3.7/dist-packages (from imagecorruptions) (1.19.5)
Requirement already satisfied: scikit-image>=0.15 in /usr/local/lib/python3.7/dist-packages (from imagecorruptions) (0.18.3)
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Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.15->imagecorruptions) (3.0.7)
Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.15->imagecorruptions) (2.8.2)
Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.15->imagecorruptions) (1.3.2)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.15->imagecorruptions) (0.11.0)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib!=3.0.0,>=2.0.0->scikit-image>=0.15->imagecorruptions) (1.15.0)
Installing collected packages: imagecorruptions
Successfully installed imagecorruptions-1.1.2
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
%matplotlib inline
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications import VGG16
from tensorflow.keras import layers
from tensorflow.keras import models
from tensorflow.keras import regularizers,optimizers
from keras.models import Sequential
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Input
from tensorflow.keras.models import Model
from keras.layers import Conv2D, MaxPooling2D,Activation
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.preprocessing.image import load_img
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
import pickle # For saving models to disk and loading saved models
import os
import zipfile
import PIL
import PIL.Image
import pathlib
import cv2
from sklearn.decomposition import PCA
from scipy.stats import stats
import imgaug as ia
ia.seed(1)
from imgaug.augmentables.bbs import BoundingBox, BoundingBoxesOnImage
from imgaug import augmenters as iaa
import imageio
import re
import shutil
load_from_original = True
compress_required = False ### Change to true when running for first time ###
csv_path = os.path.join(capstone_path,"dataframes")
try:
df_train_ann = pd.read_csv(os.path.join(csv_path,"df_train_ann.csv"))
df_test_ann = pd.read_csv(os.path.join(csv_path,"df_test_ann.csv"))
df_train = pd.read_csv(os.path.join(csv_path,"df_train.csv"))
df_val = pd.read_csv(os.path.join(csv_path,"df_val.csv"))
df_car_name = pd.read_csv(os.path.join(csv_path,"df_car_name.csv"))
load_from_original = False
print("The data will be loaded from saved files!!!")
except:
print("The data will be loaded from original files!!!")
pass
The data will be loaded from saved files!!!
load_from_original
False
### Define various dataframes and constants
BOX_COLOR = (255, 0, 0) # Red
TEXT_COLOR = (0, 0, 0) # White
if load_from_original == True:
car_img_path = os.path.join(path,'Car Images/')
car_ann_path = os.path.join(path,'Annotations/')
df_car_name = pd.read_csv("Car names and make.csv",names=['Car Name'])
train_img_dir = os.path.join(car_img_path,'Train Images')
test_img_dir = os.path.join(car_img_path,'Test Images')
df_train_ann = pd.read_csv(os.path.join(car_ann_path,'Train Annotations.csv'),names=['filename','xmin','ymin','xmax','ymax','class'])
df_test_ann = pd.read_csv(os.path.join(car_ann_path,'Test Annotation.csv'),names=['filename','xmin','ymin','xmax','ymax','class'])
df_train_ann.drop([df_train_ann.index[0]],inplace=True)
df_test_ann.drop([df_test_ann.index[0]],inplace=True)
df_train_ann.reset_index(inplace=True,drop=True)
df_test_ann.reset_index(inplace=True,drop=True)
### Create a dictionary of car names and car classes
count = 0
car_dict = {}
for car_name in df_car_name['Car Name'].values:
if car_name == 'Ram C/V Cargo Van Minivan 2012':
car_name = 'Ram C-V Cargo Van Minivan 2012'
car_dict[count+1] = car_name
count=count+1
### Split the car names in to car type, model and year
car_names = list(df_car_name['Car Name'].values)
car_types = []
car_year = []
car_model = []
for cars in car_names:
carname = cars.split(' ')
model_word_length = len(carname) - 3 #Remove 1 each for name od car company, type of car and model year
car_types.append(carname[len(carname)-2])
car_year.append(carname[len(carname)-1])
car_model.append(carname[0])
df_car_name['Car_Type'] = car_types
df_car_name['Model'] = car_model
df_car_name['Year'] = car_year
df_car_name['Car Name'].replace(to_replace=['Ram C/V Cargo Van Minivan 2012'],value='Ram C-V Cargo Van Minivan 2012',inplace=True)
df_car_name.sample(10)
| Car Name | Car_Type | Model | Year | |
|---|---|---|---|---|
| 19 | Audi S6 Sedan 2011 | Sedan | Audi | 2011 |
| 124 | HUMMER H2 SUT Crew Cab 2009 | Cab | HUMMER | 2009 |
| 40 | Bentley Mulsanne Sedan 2011 | Sedan | Bentley | 2011 |
| 155 | Lincoln Town Car Sedan 2011 | Sedan | Lincoln | 2011 |
| 100 | Ferrari FF Coupe 2012 | Coupe | Ferrari | 2012 |
| 33 | BMW M3 Coupe 2012 | Coupe | BMW | 2012 |
| 143 | Jaguar XK XKR 2012 | XKR | Jaguar | 2012 |
| 133 | Hyundai Sonata Hybrid Sedan 2012 | Sedan | Hyundai | 2012 |
| 91 | Dodge Magnum Wagon 2008 | Wagon | Dodge | 2008 |
| 181 | Suzuki Kizashi Sedan 2012 | Sedan | Suzuki | 2012 |
### Below function to get train/test images path from multiple folders
### INPUT - parent folder name
### OUTPUT - a list with full path name for each of the images
def load_images_from_folder(folder):
file_path = {}
count = 0
for folders in os.listdir(folder):
#print(folders)
if folders == '.DS_Store':
pass
else:
foldername = os.path.join(folder,folders)
#print(foldername)
for filename in os.listdir(foldername):
count = count+1
filepath = os.path.join(foldername,filename)
file_path[filename]=filepath
#print(filename)
return file_path
### Below function to do PCA transformation of images to reduce dimesionality.
### INPUT - image and n_components (default is 50)
### OUTPUT - compressed images returned.
def compress_pca(image,PCA_n_component=50):
img = cv2.imread(image,cv2.COLOR_BGR2RGB)
try:
if(len(img.shape) < 3):
print(len(img.shape))
print("\n file_path: ",image)
gray = cv2.imread(image,0)
blue=red=green=gray
else:
red,green,blue = cv2.split(img)
except:
Print("Exception encountered")
try:
df_blue = blue/255
df_green = green/255
df_red = red/255
pca_b = PCA(n_components=PCA_n_component)
pca_b.fit(df_blue)
trans_pca_b = pca_b.transform(df_blue)
pca_g = PCA(n_components=PCA_n_component)
pca_g.fit(df_green)
trans_pca_g = pca_g.transform(df_green)
pca_r = PCA(n_components=PCA_n_component)
pca_r.fit(df_red)
trans_pca_r = pca_r.transform(df_red)
b_arr = pca_b.inverse_transform(trans_pca_b)
g_arr = pca_g.inverse_transform(trans_pca_g)
r_arr = pca_r.inverse_transform(trans_pca_r)
img_reduced= (cv2.merge((r_arr, g_arr, b_arr)))
except:
return None
return img_reduced
### Below function a wrapper function for compressing a list of images using PCA transformation
### INPUT - list of images, path where to store transformed images and n_components for PCA transformation
### OUTPUT - output a successful message if completed the operation successfully else return error
def compress_image(image_list,resize_path,PCA_n_component=50):
filelist = os.listdir(resize_path)
for img in image_list:
#print(img)
img_path = img.split('/')
if img_path[-1] in filelist:
pass
else:
im = compress_pca(img)
#print(img_path[-1])
#if img_path[-1] == "00001.jpg":
# plt.imshow(im)
r_path = os.path.join(resize_path,img_path[-1])
#print(r_path)
if im is not None:
im = cv2.convertScaleAbs(im, alpha=(255.0))
if not cv2.imwrite(r_path, im):
raise Exception("Could not write image")
cv2.waitKey()
cv2.destroyAllWindows()
else:
print("image skipped:",img_path[-1])
print("The list of images compressed in folder: ", resize_path)
compress_required = False
return
### Below function will calculate the height and width of images path present in a dataframe
### INPUT - dataframe and column name of image path column
### OUTPUT - height and width list
def get_image_dims(df,col):
height = []
width = []
for img_path in df[col].values:
img = cv.imread(img_path)
height.append(int(img.shape[0]))
width.append(int(img.shape[1]))
return height,width
### Below function will plot the training and validation loss and accuracy.
### INPUT - model history and metrics
### OUTPUT - plot loss and accuracy curves
def plot_curves(history,metrics):
nrows = 1
ncols = 2
fig = plt.figure(figsize = (10,5))
for idx,key in enumerate(metrics):
ax = fig.add_subplot(nrows,ncols,idx+1)
plt.plot(history.history[key])
plt.plot(history.history['val_{}'.format(key)])
plt.title('model {}'.format(key))
plt.ylabel(key)
plt.xlabel('epoch')
plt.legend(['train','validation'],loc='upper left')
### Below function will resize images with given size without losing image's aspect ratio and store in the output folder provided
### INPUT - list of images, path where to store transformed images and image size (default 224)
### OUTPUT - None
def resize_image(image_list,resize_path,image_size=224):
for img in image_list:
#print(img)
im = cv2.imread(img,cv2.COLOR_BGR2RGB)
old_size = im.shape[:2] # old_size is in (height, width) format
ratio = float(image_size) / max(old_size)
new_size = tuple([int(x * ratio) for x in old_size])
# new_size should be in (width, height) format
im = cv2.resize(im, (new_size[1], new_size[0]))
delta_w = image_size - new_size[1]
delta_h = image_size - new_size[0]
top, bottom = delta_h // 2, delta_h - (delta_h // 2)
left, right = delta_w // 2, delta_w - (delta_w // 2)
color = [0, 0, 0]
new_im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT,
value=color)
#print(new_im.shape)
img_path = img.split('\\')
#print(img_path[-1])
r_path = os.path.join(resize_path,img_path[-1])
print(r_path)
cv2.imwrite(r_path, new_im)
cv2.waitKey(0)
cv2.destroyAllWindows()
return
#img_list = list(df_train["Image_Path"])
#compress_image(img_list,"compress_train/")
#img_list = list(df_test["Image_Path"])
#compress_image(img_list,"compress_test/")
# Function to convert bounding box image into DataFrame
def bounding_boxes_to_df(bounding_boxes_object):
# Convert Bounding Boxes Object to Array
bounding_boxes_array = bounding_boxes_object.to_xyxy_array()
# Convert the array into DataFrame
df_bounding_boxes = pd.DataFrame(bounding_boxes_array,
columns=['xmin', 'ymin', 'xmax', 'ymax'])
# Return the DataFrame
return df_bounding_boxes
# Define all the Augmentations we want to apply to the dataset
# We're setting random `n` agumentations to 2.
image_augmentations = iaa.SomeOf( 2,
[
# Scale the Images
iaa.Affine(scale=(0.5, 1.5)),
# Rotate the Images
iaa.Affine(rotate=(-60, 60)),
# Shift the Image
iaa.Affine(translate_percent={"x":(-0.3, 0.3),"y":(-0.3, 0.3)}),
# Flip the Image
iaa.Fliplr(1),
# Increase or decrease the brightness
iaa.Multiply((0.5, 1.5)),
# Add Gaussian Blur
iaa.GaussianBlur(sigma=(1.0, 3.0)),
# Add Gaussian Noise
iaa.AdditiveGaussianNoise(scale=(0.03*255, 0.05*255))
])
### Function to augment images and store in the file path input.
def image_aug(df, images_path, aug_images_path, augmentor, multiple = 3):
# Fill this DataFrame with image attributes
augmentations_df = pd.DataFrame(columns=['filename','width','height','class', 'xmin', 'ymin', 'xmax', 'ymax'])
# Group the data by filenames
grouped_df = df.groupby('filename')
# Create the directory for all augmentated images
if not os.path.exists(aug_images_path):
os.mkdir(aug_images_path)
for i in range(multiple):
# Post Fix we add to the each different augmentation of one image
image_postfix = str(i)
# Loop to perform the augmentations
for filename in df['filename'].unique():
file_name = filename.split('.')
fname = file_name[0] + image_postfix + file_name[1]
augmented_path = os.path.join(aug_images_path, fname)
# Take one image at a time with its information
single_image = grouped_df.get_group(filename)
single_image = single_image.reset_index()
single_image = single_image.drop(['index'], axis=1)
# Read the image
image = imageio.imread(os.path.join(images_path, filename))
# Get bounding box
bounding_box_array = single_image.drop(['filename', 'width', 'height',
'class'], axis=1).values
# Give the bounding box to imgaug library
bounding_box = BoundingBoxesOnImage.from_xyxy_array(bounding_box_array,
shape=image.shape)
# Perform random 2 Augmentations
image_aug, bounding_box_aug = augmentor(image=image,
bounding_boxes=bounding_box)
# Discard the the bounding box going out the image completely
bounding_box_aug = bounding_box_aug.remove_out_of_image()
# Clip the bounding box that are only partially out of th image
bounding_box_aug = bounding_box_aug.clip_out_of_image()
# Get rid of the the image if bounding box was discarded
if re.findall('Image...', str(bounding_box_aug)) == ['Image([]']:
pass
else:
# Create the augmented image file
imageio.imwrite(augmented_path, image_aug)
# Update the image width and height after augmentation
info_df = single_image.drop(['xmin', 'ymin', 'xmax', 'ymax'], axis=1)
for index, _ in info_df.iterrows():
info_df.at[index, 'width'] = image_aug.shape[1]
info_df.at[index, 'height'] = image_aug.shape[0]
# Add the prefix to each image to differentiate if required
info_df['filename'] = info_df['filename'].apply(lambda x: x + image_postfix + '.jpg')
# Create the augmented bounding boxes dataframe
bounding_box_df = bounding_boxes_to_df(bounding_box_aug)
# Concatenate the filenames, height, width and bounding boxes
aug_df = pd.concat([info_df, bounding_box_df], axis=1)
# Add all the information to augmentations_df we initialized above
augmentations_df = pd.concat([augmentations_df, aug_df])
# Remove index
augmentations_df = augmentations_df.reset_index()
augmentations_df = augmentations_df.drop(['index'], axis=1)
# Return the Dataframe
return augmentations_df
#image_directory = os.path.join(os.getcwd(),"resize_size")
#augmented_images_df = image_aug(df_img_aug, image_directory, 'aug_images_resize',
# image_augmentations)
def visualize_bbox(img, bbox, class_name, color=BOX_COLOR, thickness=2):
"""Visualizes a single bounding box on the image"""
x_min, y_min, x_max, y_max = map(int,bbox)
cv2.rectangle(img, (x_min, y_min), (x_max, y_max), color=BOX_COLOR, thickness=thickness)
((text_width, text_height), _) = cv2.getTextSize(class_name, cv2.FONT_HERSHEY_SIMPLEX, 0.35, 1)
cv2.rectangle(img, (x_min, (y_min - int(0.3 * text_height))), (x_min + text_width, y_min), BOX_COLOR, -1)
cv2.putText(
img,
text=class_name,
org=(x_min, y_min - int(0.3 * text_height)),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=0.35,
color=TEXT_COLOR,
lineType=cv2.LINE_AA,
)
return img
def visualize(image_list, bbox_list, class_list,normalize=False):
count = 0
image_final = []
for image_name in image_list:
image = cv2.imread(image_name,cv2.COLOR_BGR2RGB)
img = image.copy()
h,w,_ = img.shape
bbox = bbox_list[count]
if normalize == True:
bbox[0] = bbox[0]*w
bbox[1] = bbox[1]*h
bbox[2] = bbox[2]*w
bbox[3] = bbox[3]*h
class_name = class_list[count]
img = visualize_bbox(img, bbox, str(class_name))
image_final.append(img)
count = count + 1
fig, axs = plt.subplots(int(count), 1,figsize=(70, 70))
axs = axs.flatten()
for img,ax in zip(image_final,axs):
ax.imshow(img)
plt.show()
# YOLO requires the normalized height and width format.
def YOLO_xywh(xmin, ymin, xmax, ymax, imgw,imgh):
xmin = float(xmin)
ymin = float(ymin)
xmax = float(xmax)
ymax = float(ymax)
imgw = float(imgw)
imgh = float(imgh)
# print (float(xmin), float(ymin), float(xmax), float(ymax), float(imgw),float(imgh))
xcen = float((xmin + xmax)) / 2 / imgw
ycen = float((ymin + ymax)) / 2 / imgh
w = float((xmax - xmin)) / imgw
h = float((ymax - ymin)) / imgh
return xcen, ycen, w, h
df_train_ann
| filename | width | height | class | xmin | xmax | ymin | ymax | class_name | filepath | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 00001.jpg | 600 | 400 | 14 | 39.0 | 569.0 | 116.0 | 375.0 | Audi TTS Coupe 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 1 | 00002.jpg | 900 | 675 | 3 | 36.0 | 868.0 | 116.0 | 587.0 | Acura TL Sedan 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 2 | 00003.jpg | 640 | 480 | 91 | 85.0 | 601.0 | 109.0 | 381.0 | Dodge Dakota Club Cab 2007 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 3 | 00004.jpg | 2100 | 1386 | 134 | 621.0 | 1484.0 | 393.0 | 1096.0 | Hyundai Sonata Hybrid Sedan 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 4 | 00005.jpg | 144 | 108 | 106 | 14.0 | 133.0 | 36.0 | 99.0 | Ford F-450 Super Duty Crew Cab 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 8139 | 08140.jpg | 424 | 360 | 78 | 3.0 | 423.0 | 44.0 | 336.0 | Chrysler Town and Country Minivan 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 8140 | 08141.jpg | 800 | 600 | 196 | 138.0 | 706.0 | 150.0 | 523.0 | smart fortwo Convertible 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 8141 | 08142.jpg | 750 | 469 | 163 | 26.0 | 660.0 | 246.0 | 449.0 | Mercedes-Benz SL-Class Coupe 2009 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 8142 | 08143.jpg | 1600 | 1067 | 112 | 78.0 | 1489.0 | 526.0 | 908.0 | Ford GT Coupe 2006 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 8143 | 08144.jpg | 883 | 683 | 17 | 20.0 | 862.0 | 240.0 | 677.0 | Audi 100 Sedan 1994 | /content/drive/My Drive/capstone/Dataset/Car I... |
8144 rows × 10 columns
if load_from_original == True:
train_images_path = load_images_from_folder(train_img_dir)
test_images_path = load_images_from_folder(test_img_dir)
df_train_ann['filepath'] = df_train_ann['filename'].apply(lambda x:train_images_path[x])
df_train_ann['class_name'] = df_train_ann['class'].apply(lambda x:car_dict[int(x)])
df_train_ann['xmin'] = df_train_ann.xmin.astype(float)
#df_train_ann['class'] = df_train_ann.'class'.astype(int)
#ds_train_ann['Bbox'] = ds_train_ann.apply(lambda x:list([x['Start_x'],x['Start_y'],x['End_x'],x['End_y']]),axis=1)
df_test_ann['filepath'] = df_test_ann['filename'].apply(lambda x:test_images_path[x])
df_test_ann['xmin'] = df_test_ann.xmin.astype(float)
#df_test_ann['class'] = df_test_ann.'class'.astype(int)
df_test_ann['class_name'] = df_test_ann['class'].apply(lambda x:car_dict[int(x)])
#df_test_ann['Bbox'] = ds_test_ann.apply(lambda x:list([x['Start_x'],x['Start_y'],x['End_x'],x['End_y']]),axis=1)
if load_from_original == True:
trainh,trainw = get_image_dims(df_train_ann,'filepath')
df_train_ann['width'] = trainw
df_train_ann['height'] = trainh
testh,testw = get_image_dims(df_test_ann,'filepath')
df_test_ann['width'] = testw
df_test_ann['height'] = testh
col_list = ['filename','width','height','class','xmin','xmax','ymin','ymax','class_name','filepath']
df_train_ann = df_train_ann[col_list]
df_test_ann = df_test_ann[col_list]
df_train_ann.sample(5)
| filename | width | height | class | xmin | xmax | ymin | ymax | class_name | filepath | |
|---|---|---|---|---|---|---|---|---|---|---|
| 3249 | 03250.jpg | 640 | 426 | 176 | 366.0 | 602.0 | 163.0 | 301.0 | Rolls-Royce Ghost Sedan 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 1072 | 01073.jpg | 555 | 370 | 24 | 8.0 | 545.0 | 63.0 | 314.0 | Audi S4 Sedan 2007 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 2060 | 02061.jpg | 688 | 456 | 183 | 43.0 | 646.0 | 110.0 | 427.0 | Suzuki SX4 Hatchback 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 553 | 00554.jpg | 320 | 240 | 138 | 41.0 | 299.0 | 38.0 | 190.0 | Hyundai Sonata Sedan 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 4512 | 04513.jpg | 400 | 250 | 110 | 21.0 | 382.0 | 31.0 | 228.0 | Ford Edge SUV 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
df_test_ann.sample(5)
| filename | width | height | class | xmin | xmax | ymin | ymax | class_name | filepath | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2566 | 02567.jpg | 495 | 223 | 152 | 42.0 | 471.0 | 65.0 | 197.0 | Lamborghini Gallardo LP 570-4 Superleggera 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 7898 | 07899.jpg | 300 | 225 | 148 | 28.0 | 282.0 | 18.0 | 183.0 | Jeep Grand Cherokee SUV 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 3337 | 03338.jpg | 610 | 404 | 103 | 36.0 | 569.0 | 135.0 | 365.0 | Ferrari 458 Italia Convertible 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 1990 | 01991.jpg | 627 | 448 | 135 | 94.0 | 530.0 | 158.0 | 421.0 | Hyundai Elantra Sedan 2007 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 3732 | 03733.jpg | 1024 | 768 | 51 | 70.0 | 936.0 | 120.0 | 619.0 | Cadillac CTS-V Sedan 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
from sklearn.model_selection import train_test_split
if load_from_original == True:
df_train,df_val = train_test_split(df_train_ann,test_size=0.2,random_state=1,stratify=df_train_ann['class'])
df_train.shape
(6515, 10)
df_train.sample(5)
| filename | width | height | class | xmin | xmax | ymin | ymax | class_name | filepath | |
|---|---|---|---|---|---|---|---|---|---|---|
| 6002 | 07010.jpg | 560 | 373 | 120 | 118.0 | 485.0 | 112.0 | 351.0 | GMC Yukon Hybrid SUV 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 2154 | 05689.jpg | 655 | 261 | 132 | 27.0 | 509.0 | 8.0 | 255.0 | Hyundai Tucson SUV 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 6194 | 01758.jpg | 640 | 480 | 114 | 1.0 | 639.0 | 103.0 | 480.0 | Ford F-150 Regular Cab 2007 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 2134 | 05412.jpg | 434 | 326 | 83 | 57.0 | 378.0 | 114.0 | 253.0 | Dodge Caliber Wagon 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 758 | 03018.jpg | 640 | 480 | 117 | 43.0 | 559.0 | 110.0 | 452.0 | Ford Fiesta Sedan 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
df_val.shape
(1629, 10)
df_val.sample(5)
| filename | width | height | class | xmin | xmax | ymin | ymax | class_name | filepath | |
|---|---|---|---|---|---|---|---|---|---|---|
| 717 | 00478.jpg | 112 | 84 | 174 | 9.0 | 98.0 | 7.0 | 81.0 | Ram C-V Cargo Van Minivan 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 238 | 01456.jpg | 500 | 375 | 120 | 35.0 | 464.0 | 63.0 | 331.0 | GMC Yukon Hybrid SUV 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 396 | 01316.jpg | 429 | 262 | 57 | 20.0 | 406.0 | 35.0 | 236.0 | Chevrolet Corvette Ron Fellows Edition Z06 2007 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 1558 | 02910.jpg | 300 | 165 | 56 | 38.0 | 250.0 | 35.0 | 145.0 | Chevrolet Corvette ZR1 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 259 | 04408.jpg | 640 | 480 | 75 | 27.0 | 626.0 | 135.0 | 398.0 | Chevrolet Silverado 1500 Regular Cab 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
print(list(df_train['class'].value_counts()))
print(list(df_val['class'].value_counts()))
print(list(df_test_ann['class'].value_counts()))
[54, 39, 38, 38, 38, 38, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 29, 29, 29, 29, 29, 28, 28, 28, 27, 27, 27, 27, 27, 27, 27, 26, 25, 24, 23, 23, 19] [14, 10, 10, 10, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 6, 6, 5, 5] [68, 48, 48, 47, 46, 46, 46, 46, 46, 46, 46, 46, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 37, 37, 37, 37, 37, 36, 36, 36, 36, 36, 36, 36, 35, 35, 35, 35, 35, 34, 34, 34, 33, 33, 33, 32, 32, 32, 30, 29, 29, 27, 24]
print(len(list(df_train['class'].value_counts())))
print(len(list(df_val['class'].value_counts())))
print(len(list(df_test_ann['class'].value_counts())))
196 196 196
if load_from_original == True:
csv_path = os.path.join(capstone_path,"dataframes")
df_train_ann.to_csv(os.path.join(csv_path,"df_train_ann.csv"),index=None)
df_test_ann.to_csv(os.path.join(csv_path,"df_test_ann.csv"),index=None)
df_train.to_csv(os.path.join(csv_path,"df_train.csv"),index=None)
df_val.to_csv(os.path.join(csv_path,"df_val.csv"),index=None)
df_car_name.to_csv(os.path.join(csv_path,"df_car_name.csv"),index=None)
load_from_original = False
df_visualize = df_train_ann[['filepath','class_name','xmin','ymin','xmax','ymax']].sample(10)
df_visualize['Bbox'] = df_visualize.apply(lambda x:list([float(x['xmin']),float(x['ymin']),float(x['xmax']),float(x['ymax'])]),axis=1)
df_visualize.drop(columns=['xmin','ymin','xmax','ymax'],inplace=True)
df_visualize
| filepath | class_name | Bbox | |
|---|---|---|---|
| 4299 | /content/drive/My Drive/capstone/Dataset/Car I... | Chrysler Aspen SUV 2009 | [16.0, 29.0, 241.0, 155.0] |
| 2723 | /content/drive/My Drive/capstone/Dataset/Car I... | Ford Freestar Minivan 2007 | [38.0, 18.0, 192.0, 110.0] |
| 4127 | /content/drive/My Drive/capstone/Dataset/Car I... | Volkswagen Beetle Hatchback 2012 | [71.0, 65.0, 579.0, 396.0] |
| 1381 | /content/drive/My Drive/capstone/Dataset/Car I... | Cadillac Escalade EXT Crew Cab 2007 | [3.0, 21.0, 285.0, 195.0] |
| 1800 | /content/drive/My Drive/capstone/Dataset/Car I... | Dodge Durango SUV 2007 | [15.0, 25.0, 239.0, 182.0] |
| 8005 | /content/drive/My Drive/capstone/Dataset/Car I... | Hyundai Azera Sedan 2012 | [30.0, 146.0, 666.0, 378.0] |
| 4619 | /content/drive/My Drive/capstone/Dataset/Car I... | Chrysler PT Cruiser Convertible 2008 | [30.0, 183.0, 503.0, 383.0] |
| 6013 | /content/drive/My Drive/capstone/Dataset/Car I... | FIAT 500 Convertible 2012 | [274.0, 376.0, 1120.0, 826.0] |
| 1827 | /content/drive/My Drive/capstone/Dataset/Car I... | Buick Verano Sedan 2012 | [70.0, 105.0, 443.0, 285.0] |
| 1193 | /content/drive/My Drive/capstone/Dataset/Car I... | Geo Metro Convertible 1993 | [62.0, 141.0, 309.0, 246.0] |
image_list = list(df_visualize['filepath'])
class_list = list(df_visualize['class_name'])
bbox_list = list(df_visualize['Bbox'])
visualize(image_list,bbox_list,class_list)
if compress_required == True:
comp_image_path = os.path.join(capstone_path,"compressed_images")
img_list = list(df_train["filepath"])
compress_image(img_list,comp_image_path)
compress_required = True
comp_image_path = os.path.join(capstone_path,"compressed_val_images")
img_list = list(df_val["filepath"])
compress_image(img_list,comp_image_path)
compress_required = True
comp_image_path = os.path.join(capstone_path,"compressed_test_images")
img_list = list(df_test_ann["filepath"])
compress_image(img_list,comp_image_path)
compress_required = False
!python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
2022-01-30 08:03:18.822503: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0. tf.Tensor(-1019.2014, shape=(), dtype=float32)
os.chdir(capstone_path)
%cd tensorflow
/content/drive/My Drive/capstone/tensorflow
#!git clone https://github.com/tensorflow/models.git
%cd ./models/research/
/content/drive/My Drive/capstone/tensorflow/models/research
!protoc object_detection/protos/*.proto --python_out=.
#!git clone https://github.com/cocodataset/cocoapi.git
#%cd cocoapi/PythonAPI
#!make
#!cp -r pycocotools ../../
!pwd
/content/drive/MyDrive/capstone/tensorflow/models/research
#cd workspace/training-yolo/
#cd yolov5
!ls
adversarial_text deep_speech pcl_rl attention_ocr delf pycocotools audioset efficient-hrl README.md autoaugment lfads rebar build lstm_object_detection seq_flow_lite cocoapi marco setup.py cognitive_planning nst_blogpost slim cvt_text object_detection vid2depth deeplab object_detection.egg-info
!pwd
/content/drive/MyDrive/capstone/tensorflow/models/research
!cp object_detection/packages/tf2/setup.py .
#!python setup.py install
!python -m pip install --use-feature=in-tree-build .
Processing /content/drive/MyDrive/capstone/tensorflow/models/research
Requirement already satisfied: avro-python3 in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (1.10.2)
Requirement already satisfied: apache-beam in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (2.35.0)
Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (7.1.2)
Requirement already satisfied: lxml in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (4.2.6)
Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (3.2.2)
Requirement already satisfied: Cython in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (0.29.26)
Requirement already satisfied: contextlib2 in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (0.5.5)
Requirement already satisfied: tf-slim in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (1.1.0)
Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (1.15.0)
Requirement already satisfied: pycocotools in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (2.0.4)
Requirement already satisfied: lvis in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (0.5.3)
Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (1.4.1)
Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (1.1.5)
Requirement already satisfied: tf-models-official>=2.5.1 in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (2.7.0)
Requirement already satisfied: tensorflow_io in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (0.23.1)
Requirement already satisfied: keras in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (2.7.0)
Requirement already satisfied: opencv-python-headless in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (4.1.2.30)
Requirement already satisfied: tensorflow-text>=2.7.0 in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (2.7.3)
Requirement already satisfied: sacrebleu in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (2.0.0)
Requirement already satisfied: psutil>=5.4.3 in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (5.4.8)
Requirement already satisfied: kaggle>=1.3.9 in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (1.5.12)
Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (6.0)
Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (1.19.5)
Requirement already satisfied: oauth2client in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (4.1.3)
Requirement already satisfied: google-api-python-client>=1.6.7 in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (1.12.10)
Requirement already satisfied: seqeval in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (1.2.2)
Requirement already satisfied: tensorflow>=2.7.0 in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (2.7.0)
Requirement already satisfied: gin-config in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (0.5.0)
Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (0.15.0)
Requirement already satisfied: sentencepiece in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (0.1.96)
Requirement already satisfied: tensorflow-hub>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (0.12.0)
Requirement already satisfied: py-cpuinfo>=3.3.0 in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (8.0.0)
Requirement already satisfied: tensorflow-datasets in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (4.0.1)
Requirement already satisfied: tensorflow-model-optimization>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tf-models-official>=2.5.1->object-detection==0.1) (0.7.0)
Requirement already satisfied: google-api-core<3dev,>=1.21.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (1.26.3)
Requirement already satisfied: google-auth<3dev,>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (1.35.0)
Requirement already satisfied: uritemplate<4dev,>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (3.0.1)
Requirement already satisfied: httplib2<1dev,>=0.15.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (0.17.4)
Requirement already satisfied: google-auth-httplib2>=0.0.3 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (0.0.4)
Requirement already satisfied: packaging>=14.3 in /usr/local/lib/python3.7/dist-packages (from google-api-core<3dev,>=1.21.0->google-api-python-client>=1.6.7->tf-models-official>=2.5.1->object-detection==0.1) (21.3)
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Building wheels for collected packages: object-detection
Building wheel for object-detection (setup.py) ... done
Created wheel for object-detection: filename=object_detection-0.1-py3-none-any.whl size=21906139 sha256=a8c23bf6ebdd617de5a4aa8ba513d8841d3f193bed80cbbbcba88351852f7a34
Stored in directory: /tmp/pip-ephem-wheel-cache-jucvrkqa/wheels/3a/7d/30/e990abb534ba8f86da363004cce7f0d15ec0d2ad6a35117be8
Successfully built object-detection
Installing collected packages: object-detection
Attempting uninstall: object-detection
Found existing installation: object-detection 0.1
Uninstalling object-detection-0.1:
Successfully uninstalled object-detection-0.1
Successfully installed object-detection-0.1
!python object_detection/builders/model_builder_tf2_test.py
Running tests under Python 3.7.12: /usr/bin/python3 [ RUN ] ModelBuilderTF2Test.test_create_center_net_deepmac 2022-01-30 08:05:54.809461: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0. W0130 08:05:55.146317 140098475657088 model_builder.py:1100] Building experimental DeepMAC meta-arch. Some features may be omitted. INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_deepmac): 1.27s I0130 08:05:55.386464 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_center_net_deepmac): 1.27s [ OK ] ModelBuilderTF2Test.test_create_center_net_deepmac [ RUN ] ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True) INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)): 0.49s I0130 08:05:55.877207 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)): 0.49s [ OK ] ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True) [ RUN ] ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False) INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)): 0.26s I0130 08:05:56.140501 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)): 0.26s [ OK ] ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False) [ RUN ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.24s I0130 08:05:56.378103 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.24s [ OK ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints [ RUN ] ModelBuilderTF2Test.test_create_center_net_model_mobilenet INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model_mobilenet): 1.65s I0130 08:05:58.032945 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_mobilenet): 1.65s [ OK ] ModelBuilderTF2Test.test_create_center_net_model_mobilenet [ RUN ] ModelBuilderTF2Test.test_create_experimental_model INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s I0130 08:05:58.033852 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s [ OK ] ModelBuilderTF2Test.test_create_experimental_model [ RUN ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True) INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.02s I0130 08:05:58.055828 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.02s [ OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True) [ RUN ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False) INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.01s I0130 08:05:58.069534 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.01s [ OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False) [ RUN ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.01s I0130 08:05:58.083783 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.01s [ OK ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner [ RUN ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.09s I0130 08:05:58.176815 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.09s [ OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul [ RUN ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.09s I0130 08:05:58.264548 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.09s [ OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul [ RUN ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.1s I0130 08:05:58.364907 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.1s [ OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul [ RUN ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.09s I0130 08:05:58.460211 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.09s [ OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul [ RUN ] ModelBuilderTF2Test.test_create_rfcn_model_from_config INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.09s I0130 08:05:58.554832 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.09s [ OK ] ModelBuilderTF2Test.test_create_rfcn_model_from_config [ RUN ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.03s I0130 08:05:58.585132 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.03s [ OK ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config [ RUN ] ModelBuilderTF2Test.test_create_ssd_models_from_config I0130 08:05:58.761271 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet EfficientNet backbone version: efficientnet-b0 I0130 08:05:58.761420 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 64 I0130 08:05:58.761470 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:149] EfficientDet BiFPN num iterations: 3 I0130 08:05:58.763537 140098475657088 efficientnet_model.py:147] round_filter input=32 output=32 I0130 08:05:58.779495 140098475657088 efficientnet_model.py:147] round_filter input=32 output=32 I0130 08:05:58.779605 140098475657088 efficientnet_model.py:147] round_filter input=16 output=16 I0130 08:05:58.836241 140098475657088 efficientnet_model.py:147] round_filter input=16 output=16 I0130 08:05:58.836394 140098475657088 efficientnet_model.py:147] round_filter input=24 output=24 I0130 08:05:58.977824 140098475657088 efficientnet_model.py:147] round_filter input=24 output=24 I0130 08:05:58.977968 140098475657088 efficientnet_model.py:147] round_filter input=40 output=40 I0130 08:05:59.112708 140098475657088 efficientnet_model.py:147] round_filter input=40 output=40 I0130 08:05:59.112871 140098475657088 efficientnet_model.py:147] round_filter input=80 output=80 I0130 08:05:59.460819 140098475657088 efficientnet_model.py:147] round_filter input=80 output=80 I0130 08:05:59.460990 140098475657088 efficientnet_model.py:147] round_filter input=112 output=112 I0130 08:05:59.666291 140098475657088 efficientnet_model.py:147] round_filter input=112 output=112 I0130 08:05:59.666456 140098475657088 efficientnet_model.py:147] round_filter input=192 output=192 I0130 08:05:59.937867 140098475657088 efficientnet_model.py:147] round_filter input=192 output=192 I0130 08:05:59.938024 140098475657088 efficientnet_model.py:147] round_filter input=320 output=320 I0130 08:06:00.003347 140098475657088 efficientnet_model.py:147] round_filter input=1280 output=1280 I0130 08:06:00.030011 140098475657088 efficientnet_model.py:457] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.0, resolution=224, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32') I0130 08:06:00.081468 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet EfficientNet backbone version: efficientnet-b1 I0130 08:06:00.081606 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 88 I0130 08:06:00.081659 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:149] EfficientDet BiFPN num iterations: 4 I0130 08:06:00.083197 140098475657088 efficientnet_model.py:147] round_filter input=32 output=32 I0130 08:06:00.097352 140098475657088 efficientnet_model.py:147] round_filter input=32 output=32 I0130 08:06:00.097465 140098475657088 efficientnet_model.py:147] round_filter input=16 output=16 I0130 08:06:00.205763 140098475657088 efficientnet_model.py:147] round_filter input=16 output=16 I0130 08:06:00.205906 140098475657088 efficientnet_model.py:147] round_filter input=24 output=24 I0130 08:06:00.412404 140098475657088 efficientnet_model.py:147] round_filter input=24 output=24 I0130 08:06:00.412569 140098475657088 efficientnet_model.py:147] round_filter input=40 output=40 I0130 08:06:00.621731 140098475657088 efficientnet_model.py:147] round_filter input=40 output=40 I0130 08:06:00.621893 140098475657088 efficientnet_model.py:147] round_filter input=80 output=80 I0130 08:06:00.900961 140098475657088 efficientnet_model.py:147] round_filter input=80 output=80 I0130 08:06:00.901120 140098475657088 efficientnet_model.py:147] round_filter input=112 output=112 I0130 08:06:01.176057 140098475657088 efficientnet_model.py:147] round_filter input=112 output=112 I0130 08:06:01.176220 140098475657088 efficientnet_model.py:147] round_filter input=192 output=192 I0130 08:06:01.517135 140098475657088 efficientnet_model.py:147] round_filter input=192 output=192 I0130 08:06:01.517295 140098475657088 efficientnet_model.py:147] round_filter input=320 output=320 I0130 08:06:01.653026 140098475657088 efficientnet_model.py:147] round_filter input=1280 output=1280 I0130 08:06:01.681439 140098475657088 efficientnet_model.py:457] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.1, resolution=240, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32') I0130 08:06:01.739365 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet EfficientNet backbone version: efficientnet-b2 I0130 08:06:01.739523 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 112 I0130 08:06:01.739573 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:149] EfficientDet BiFPN num iterations: 5 I0130 08:06:01.741083 140098475657088 efficientnet_model.py:147] round_filter input=32 output=32 I0130 08:06:01.754995 140098475657088 efficientnet_model.py:147] round_filter input=32 output=32 I0130 08:06:01.755105 140098475657088 efficientnet_model.py:147] round_filter input=16 output=16 I0130 08:06:01.868809 140098475657088 efficientnet_model.py:147] round_filter input=16 output=16 I0130 08:06:01.868936 140098475657088 efficientnet_model.py:147] round_filter input=24 output=24 I0130 08:06:02.075141 140098475657088 efficientnet_model.py:147] round_filter input=24 output=24 I0130 08:06:02.075299 140098475657088 efficientnet_model.py:147] round_filter input=40 output=48 I0130 08:06:02.280961 140098475657088 efficientnet_model.py:147] round_filter input=40 output=48 I0130 08:06:02.281124 140098475657088 efficientnet_model.py:147] round_filter input=80 output=88 I0130 08:06:02.564807 140098475657088 efficientnet_model.py:147] round_filter input=80 output=88 I0130 08:06:02.564962 140098475657088 efficientnet_model.py:147] round_filter input=112 output=120 I0130 08:06:02.846698 140098475657088 efficientnet_model.py:147] round_filter input=112 output=120 I0130 08:06:02.846854 140098475657088 efficientnet_model.py:147] round_filter input=192 output=208 I0130 08:06:03.369178 140098475657088 efficientnet_model.py:147] round_filter input=192 output=208 I0130 08:06:03.369354 140098475657088 efficientnet_model.py:147] round_filter input=320 output=352 I0130 08:06:03.505091 140098475657088 efficientnet_model.py:147] round_filter input=1280 output=1408 I0130 08:06:03.533818 140098475657088 efficientnet_model.py:457] Building model efficientnet with params ModelConfig(width_coefficient=1.1, depth_coefficient=1.2, resolution=260, dropout_rate=0.3, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32') I0130 08:06:03.590519 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet EfficientNet backbone version: efficientnet-b3 I0130 08:06:03.590692 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 160 I0130 08:06:03.590770 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:149] EfficientDet BiFPN num iterations: 6 I0130 08:06:03.592720 140098475657088 efficientnet_model.py:147] round_filter input=32 output=40 I0130 08:06:03.607010 140098475657088 efficientnet_model.py:147] round_filter input=32 output=40 I0130 08:06:03.607107 140098475657088 efficientnet_model.py:147] round_filter input=16 output=24 I0130 08:06:03.721020 140098475657088 efficientnet_model.py:147] round_filter input=16 output=24 I0130 08:06:03.721172 140098475657088 efficientnet_model.py:147] round_filter input=24 output=32 I0130 08:06:03.930721 140098475657088 efficientnet_model.py:147] round_filter input=24 output=32 I0130 08:06:03.930878 140098475657088 efficientnet_model.py:147] round_filter input=40 output=48 I0130 08:06:04.137856 140098475657088 efficientnet_model.py:147] round_filter input=40 output=48 I0130 08:06:04.138017 140098475657088 efficientnet_model.py:147] round_filter input=80 output=96 I0130 08:06:04.483711 140098475657088 efficientnet_model.py:147] round_filter input=80 output=96 I0130 08:06:04.483870 140098475657088 efficientnet_model.py:147] round_filter input=112 output=136 I0130 08:06:04.830614 140098475657088 efficientnet_model.py:147] round_filter input=112 output=136 I0130 08:06:04.830782 140098475657088 efficientnet_model.py:147] round_filter input=192 output=232 I0130 08:06:05.245160 140098475657088 efficientnet_model.py:147] round_filter input=192 output=232 I0130 08:06:05.245331 140098475657088 efficientnet_model.py:147] round_filter input=320 output=384 I0130 08:06:05.386172 140098475657088 efficientnet_model.py:147] round_filter input=1280 output=1536 I0130 08:06:05.412555 140098475657088 efficientnet_model.py:457] Building model efficientnet with params ModelConfig(width_coefficient=1.2, depth_coefficient=1.4, resolution=300, dropout_rate=0.3, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32') I0130 08:06:05.476022 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet EfficientNet backbone version: efficientnet-b4 I0130 08:06:05.476176 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 224 I0130 08:06:05.476250 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:149] EfficientDet BiFPN num iterations: 7 I0130 08:06:05.477810 140098475657088 efficientnet_model.py:147] round_filter input=32 output=48 I0130 08:06:05.495090 140098475657088 efficientnet_model.py:147] round_filter input=32 output=48 I0130 08:06:05.495225 140098475657088 efficientnet_model.py:147] round_filter input=16 output=24 I0130 08:06:05.607083 140098475657088 efficientnet_model.py:147] round_filter input=16 output=24 I0130 08:06:05.607237 140098475657088 efficientnet_model.py:147] round_filter input=24 output=32 I0130 08:06:05.886535 140098475657088 efficientnet_model.py:147] round_filter input=24 output=32 I0130 08:06:05.886725 140098475657088 efficientnet_model.py:147] round_filter input=40 output=56 I0130 08:06:06.163819 140098475657088 efficientnet_model.py:147] round_filter input=40 output=56 I0130 08:06:06.163996 140098475657088 efficientnet_model.py:147] round_filter input=80 output=112 I0130 08:06:06.579067 140098475657088 efficientnet_model.py:147] round_filter input=80 output=112 I0130 08:06:06.579244 140098475657088 efficientnet_model.py:147] round_filter input=112 output=160 I0130 08:06:07.010339 140098475657088 efficientnet_model.py:147] round_filter input=112 output=160 I0130 08:06:07.010557 140098475657088 efficientnet_model.py:147] round_filter input=192 output=272 I0130 08:06:07.573372 140098475657088 efficientnet_model.py:147] round_filter input=192 output=272 I0130 08:06:07.573561 140098475657088 efficientnet_model.py:147] round_filter input=320 output=448 I0130 08:06:07.952463 140098475657088 efficientnet_model.py:147] round_filter input=1280 output=1792 I0130 08:06:07.978668 140098475657088 efficientnet_model.py:457] Building model efficientnet with params ModelConfig(width_coefficient=1.4, depth_coefficient=1.8, resolution=380, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32') I0130 08:06:08.051736 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet EfficientNet backbone version: efficientnet-b5 I0130 08:06:08.051899 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 288 I0130 08:06:08.051990 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:149] EfficientDet BiFPN num iterations: 7 I0130 08:06:08.053609 140098475657088 efficientnet_model.py:147] round_filter input=32 output=48 I0130 08:06:08.067828 140098475657088 efficientnet_model.py:147] round_filter input=32 output=48 I0130 08:06:08.067955 140098475657088 efficientnet_model.py:147] round_filter input=16 output=24 I0130 08:06:08.232801 140098475657088 efficientnet_model.py:147] round_filter input=16 output=24 I0130 08:06:08.232965 140098475657088 efficientnet_model.py:147] round_filter input=24 output=40 I0130 08:06:08.582899 140098475657088 efficientnet_model.py:147] round_filter input=24 output=40 I0130 08:06:08.583068 140098475657088 efficientnet_model.py:147] round_filter input=40 output=64 I0130 08:06:08.935224 140098475657088 efficientnet_model.py:147] round_filter input=40 output=64 I0130 08:06:08.935379 140098475657088 efficientnet_model.py:147] round_filter input=80 output=128 I0130 08:06:09.412943 140098475657088 efficientnet_model.py:147] round_filter input=80 output=128 I0130 08:06:09.413106 140098475657088 efficientnet_model.py:147] round_filter input=112 output=176 I0130 08:06:09.892182 140098475657088 efficientnet_model.py:147] round_filter input=112 output=176 I0130 08:06:09.892339 140098475657088 efficientnet_model.py:147] round_filter input=192 output=304 I0130 08:06:10.524228 140098475657088 efficientnet_model.py:147] round_filter input=192 output=304 I0130 08:06:10.524397 140098475657088 efficientnet_model.py:147] round_filter input=320 output=512 I0130 08:06:10.734781 140098475657088 efficientnet_model.py:147] round_filter input=1280 output=2048 I0130 08:06:10.761371 140098475657088 efficientnet_model.py:457] Building model efficientnet with params ModelConfig(width_coefficient=1.6, depth_coefficient=2.2, resolution=456, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32') I0130 08:06:10.852889 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet EfficientNet backbone version: efficientnet-b6 I0130 08:06:10.853036 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 384 I0130 08:06:10.853110 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:149] EfficientDet BiFPN num iterations: 8 I0130 08:06:10.854681 140098475657088 efficientnet_model.py:147] round_filter input=32 output=56 I0130 08:06:10.868927 140098475657088 efficientnet_model.py:147] round_filter input=32 output=56 I0130 08:06:10.869042 140098475657088 efficientnet_model.py:147] round_filter input=16 output=32 I0130 08:06:11.036001 140098475657088 efficientnet_model.py:147] round_filter input=16 output=32 I0130 08:06:11.036173 140098475657088 efficientnet_model.py:147] round_filter input=24 output=40 I0130 08:06:11.451443 140098475657088 efficientnet_model.py:147] round_filter input=24 output=40 I0130 08:06:11.451622 140098475657088 efficientnet_model.py:147] round_filter input=40 output=72 I0130 08:06:11.870974 140098475657088 efficientnet_model.py:147] round_filter input=40 output=72 I0130 08:06:11.871149 140098475657088 efficientnet_model.py:147] round_filter input=80 output=144 I0130 08:06:12.418941 140098475657088 efficientnet_model.py:147] round_filter input=80 output=144 I0130 08:06:12.419108 140098475657088 efficientnet_model.py:147] round_filter input=112 output=200 I0130 08:06:13.214609 140098475657088 efficientnet_model.py:147] round_filter input=112 output=200 I0130 08:06:13.214803 140098475657088 efficientnet_model.py:147] round_filter input=192 output=344 I0130 08:06:13.990633 140098475657088 efficientnet_model.py:147] round_filter input=192 output=344 I0130 08:06:13.990813 140098475657088 efficientnet_model.py:147] round_filter input=320 output=576 I0130 08:06:14.198748 140098475657088 efficientnet_model.py:147] round_filter input=1280 output=2304 I0130 08:06:14.224623 140098475657088 efficientnet_model.py:457] Building model efficientnet with params ModelConfig(width_coefficient=1.8, depth_coefficient=2.6, resolution=528, dropout_rate=0.5, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32') I0130 08:06:14.322106 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet EfficientNet backbone version: efficientnet-b7 I0130 08:06:14.322283 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num filters: 384 I0130 08:06:14.322356 140098475657088 ssd_efficientnet_bifpn_feature_extractor.py:149] EfficientDet BiFPN num iterations: 8 I0130 08:06:14.323943 140098475657088 efficientnet_model.py:147] round_filter input=32 output=64 I0130 08:06:14.338570 140098475657088 efficientnet_model.py:147] round_filter input=32 output=64 I0130 08:06:14.338687 140098475657088 efficientnet_model.py:147] round_filter input=16 output=32 I0130 08:06:14.561251 140098475657088 efficientnet_model.py:147] round_filter input=16 output=32 I0130 08:06:14.561438 140098475657088 efficientnet_model.py:147] round_filter input=24 output=48 I0130 08:06:15.048639 140098475657088 efficientnet_model.py:147] round_filter input=24 output=48 I0130 08:06:15.048818 140098475657088 efficientnet_model.py:147] round_filter input=40 output=80 I0130 08:06:15.528684 140098475657088 efficientnet_model.py:147] round_filter input=40 output=80 I0130 08:06:15.528866 140098475657088 efficientnet_model.py:147] round_filter input=80 output=160 I0130 08:06:16.230118 140098475657088 efficientnet_model.py:147] round_filter input=80 output=160 I0130 08:06:16.230304 140098475657088 efficientnet_model.py:147] round_filter input=112 output=224 I0130 08:06:16.929373 140098475657088 efficientnet_model.py:147] round_filter input=112 output=224 I0130 08:06:16.929553 140098475657088 efficientnet_model.py:147] round_filter input=192 output=384 I0130 08:06:18.075128 140098475657088 efficientnet_model.py:147] round_filter input=192 output=384 I0130 08:06:18.075303 140098475657088 efficientnet_model.py:147] round_filter input=320 output=640 I0130 08:06:18.362827 140098475657088 efficientnet_model.py:147] round_filter input=1280 output=2560 I0130 08:06:18.389021 140098475657088 efficientnet_model.py:457] Building model efficientnet with params ModelConfig(width_coefficient=2.0, depth_coefficient=3.1, resolution=600, dropout_rate=0.5, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32') INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 19.92s I0130 08:06:18.504767 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 19.92s [ OK ] ModelBuilderTF2Test.test_create_ssd_models_from_config [ RUN ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s I0130 08:06:18.512332 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s [ OK ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update [ RUN ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s I0130 08:06:18.514171 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s [ OK ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold [ RUN ] ModelBuilderTF2Test.test_invalid_model_config_proto INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s I0130 08:06:18.514730 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s [ OK ] ModelBuilderTF2Test.test_invalid_model_config_proto [ RUN ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s I0130 08:06:18.516406 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s [ OK ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size [ RUN ] ModelBuilderTF2Test.test_session [ SKIPPED ] ModelBuilderTF2Test.test_session [ RUN ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s I0130 08:06:18.517955 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s [ OK ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor [ RUN ] ModelBuilderTF2Test.test_unknown_meta_architecture INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s I0130 08:06:18.518465 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s [ OK ] ModelBuilderTF2Test.test_unknown_meta_architecture [ RUN ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s I0130 08:06:18.519565 140098475657088 test_util.py:2309] time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s [ OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor ---------------------------------------------------------------------- Ran 24 tests in 24.399s OK (skipped=1)
#%cd ..
#%cd ..
#%cd workspace/training-ssd
ssd_path = os.path.join(capstone_path,"tensorflow/workspace/training-ssd")
os.chdir(ssd_path)
df_train.sample(5)
| filename | width | height | class | xmin | xmax | ymin | ymax | class_name | filepath | |
|---|---|---|---|---|---|---|---|---|---|---|
| 3803 | 04961.jpg | 750 | 469 | 154 | 84.0 | 686.0 | 89.0 | 424.0 | Land Rover Range Rover SUV 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 2638 | 05312.jpg | 480 | 320 | 45 | 154.0 | 324.0 | 125.0 | 286.0 | Bugatti Veyron 16.4 Convertible 2009 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 1981 | 04069.jpg | 300 | 225 | 84 | 22.0 | 285.0 | 55.0 | 199.0 | Dodge Caliber Wagon 2007 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 1888 | 00738.jpg | 640 | 480 | 55 | 42.0 | 602.0 | 153.0 | 389.0 | Chevrolet Corvette Convertible 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 5997 | 05517.jpg | 450 | 241 | 21 | 47.0 | 416.0 | 16.0 | 198.0 | Audi S5 Convertible 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
train_csv = df_train[['filename','width','height','class_name','xmin','ymin','xmax','ymax']]
val_csv = df_val[['filename','width','height','class_name','xmin','ymin','xmax','ymax']]
path_to_pbtext = os.path.join(ssd_path,"annotations/label_map.pbtxt")
path_tfrecord_train = os.path.join(ssd_path,"annotations/train.record")
path_tfrecord_test = os.path.join(ssd_path,"annotations/test.record")
path_to_train_images = os.path.join(ssd_path,"images/compressed_images")
path_to_test_images = os.path.join(ssd_path,"images/compressed_test_images")
path_to_train_csv = os.path.join(capstone_path,"train_csv.csv")
path_to_test_csv = os.path.join(capstone_path,"val_csv.csv")
#train_csv.to_csv(path_to_train_csv,index=None)
#val_csv.to_csv(path_to_test_csv,index=None)
train_csv = pd.read_csv(path_to_train_csv)
val_csv = pd.read_csv(path_to_test_csv)
%cd ..
/content/drive/MyDrive/capstone/tensorflow/workspace
%cd ..
/content/drive/MyDrive/capstone/tensorflow
%cd /content/drive/My Drive/capstone/tensorflow/scripts
/content/drive/My Drive/capstone/tensorflow/scripts
#!python generate_tfrecord.py -x ../workspace/training-ssd/images/compressed_images -l ../workspace/training-ssd/annotations/label_map.pbtxt -o ../workspace/training-ssd/annotations/train.record -c ../workspace/training-ssd/images/train_csv.csv
#!python generate_tfrecord.py -x ../workspace/training-ssd/images/compressed_val_images -l ../workspace/training-ssd/annotations/label_map.pbtxt -o ../workspace/training-ssd/annotations/test.record -c ../workspace/training-ssd/images/val_csv.csv
!ls
Annotations 'Car Images' 'Car names and make.csv' __MACOSX
%cd /content/drive/My Drive/capstone/tensorflow/workspace/training-ssd/
/content/drive/My Drive/capstone/tensorflow/workspace/training-ssd
!ls
annotations images 'pre-trained models' exported-models model_main_tf2.py exporter_main_v2.py models
!python model_main_tf2.py --model_dir=models/my_ssd_mobilenet --pipeline_config_path=models/my_ssd_mobilenet/pipeline.config
2022-01-23 12:20:13.368157: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
I0123 12:20:13.371561 139706229311360 mirrored_strategy.py:376] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
INFO:tensorflow:Maybe overwriting train_steps: None
I0123 12:20:14.230054 139706229311360 config_util.py:552] Maybe overwriting train_steps: None
INFO:tensorflow:Maybe overwriting use_bfloat16: False
I0123 12:20:14.230258 139706229311360 config_util.py:552] Maybe overwriting use_bfloat16: False
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/model_lib_v2.py:564: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.
Instructions for updating:
rename to distribute_datasets_from_function
W0123 12:20:14.259042 139706229311360 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/object_detection/model_lib_v2.py:564: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.
Instructions for updating:
rename to distribute_datasets_from_function
INFO:tensorflow:Reading unweighted datasets: ['./annotations/train.record']
I0123 12:20:14.674426 139706229311360 dataset_builder.py:163] Reading unweighted datasets: ['./annotations/train.record']
INFO:tensorflow:Reading record datasets for input file: ['./annotations/train.record']
I0123 12:20:14.674803 139706229311360 dataset_builder.py:80] Reading record datasets for input file: ['./annotations/train.record']
INFO:tensorflow:Number of filenames to read: 1
I0123 12:20:14.674925 139706229311360 dataset_builder.py:81] Number of filenames to read: 1
WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.
W0123 12:20:14.675002 139706229311360 dataset_builder.py:88] num_readers has been reduced to 1 to match input file shards.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:105: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.
W0123 12:20:14.681763 139706229311360 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:105: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:237: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.map()
W0123 12:20:14.709128 139706229311360 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:237: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.map()
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.
W0123 12:20:21.463398 139706229311360 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.
Instructions for updating:
`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead.
W0123 12:20:24.445254 139706229311360 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.
Instructions for updating:
`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
W0123 12:20:26.018429 139706229311360 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
2022-01-23 12:20:32.411990: W tensorflow/core/framework/op_kernel.cc:1745] OP_REQUIRES failed at multi_device_iterator_ops.cc:789 : NOT_FOUND: Resource AnonymousMultiDeviceIterator/AnonymousMultiDeviceIterator0/N10tensorflow4data12_GLOBAL__N_119MultiDeviceIteratorE does not exist.
/usr/local/lib/python3.7/dist-packages/keras/backend.py:414: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.
warnings.warn('`tf.keras.backend.set_learning_phase` is deprecated and '
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py:620: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Use fn_output_signature instead
W0123 12:21:07.224520 139700694898432 deprecation.py:551] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py:620: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Use fn_output_signature instead
INFO:tensorflow:Step 18100 per-step time 1.006s
I0123 12:22:47.513062 139706229311360 model_lib_v2.py:707] Step 18100 per-step time 1.006s
INFO:tensorflow:{'Loss/classification_loss': 0.29952532,
'Loss/localization_loss': 0.024854204,
'Loss/regularization_loss': 0.09463329,
'Loss/total_loss': 0.4190128,
'learning_rate': 0.058273777}
I0123 12:22:47.513358 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.29952532,
'Loss/localization_loss': 0.024854204,
'Loss/regularization_loss': 0.09463329,
'Loss/total_loss': 0.4190128,
'learning_rate': 0.058273777}
INFO:tensorflow:Step 18200 per-step time 0.644s
I0123 12:23:51.887348 139706229311360 model_lib_v2.py:707] Step 18200 per-step time 0.644s
INFO:tensorflow:{'Loss/classification_loss': 0.42891264,
'Loss/localization_loss': 0.011297177,
'Loss/regularization_loss': 0.094637334,
'Loss/total_loss': 0.53484714,
'learning_rate': 0.05804527}
I0123 12:23:51.887635 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.42891264,
'Loss/localization_loss': 0.011297177,
'Loss/regularization_loss': 0.094637334,
'Loss/total_loss': 0.53484714,
'learning_rate': 0.05804527}
INFO:tensorflow:Step 18300 per-step time 0.640s
I0123 12:24:55.929780 139706229311360 model_lib_v2.py:707] Step 18300 per-step time 0.640s
INFO:tensorflow:{'Loss/classification_loss': 0.36802167,
'Loss/localization_loss': 0.013548903,
'Loss/regularization_loss': 0.09461092,
'Loss/total_loss': 0.4761815,
'learning_rate': 0.05781603}
I0123 12:24:55.930065 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.36802167,
'Loss/localization_loss': 0.013548903,
'Loss/regularization_loss': 0.09461092,
'Loss/total_loss': 0.4761815,
'learning_rate': 0.05781603}
INFO:tensorflow:Step 18400 per-step time 0.640s
I0123 12:25:59.960031 139706229311360 model_lib_v2.py:707] Step 18400 per-step time 0.640s
INFO:tensorflow:{'Loss/classification_loss': 0.30406886,
'Loss/localization_loss': 0.012708182,
'Loss/regularization_loss': 0.0945678,
'Loss/total_loss': 0.41134486,
'learning_rate': 0.057586048}
I0123 12:25:59.960329 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.30406886,
'Loss/localization_loss': 0.012708182,
'Loss/regularization_loss': 0.0945678,
'Loss/total_loss': 0.41134486,
'learning_rate': 0.057586048}
INFO:tensorflow:Step 18500 per-step time 0.641s
I0123 12:27:04.089060 139706229311360 model_lib_v2.py:707] Step 18500 per-step time 0.641s
INFO:tensorflow:{'Loss/classification_loss': 0.33735645,
'Loss/localization_loss': 0.017137185,
'Loss/regularization_loss': 0.0945202,
'Loss/total_loss': 0.44901383,
'learning_rate': 0.05735535}
I0123 12:27:04.089377 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.33735645,
'Loss/localization_loss': 0.017137185,
'Loss/regularization_loss': 0.0945202,
'Loss/total_loss': 0.44901383,
'learning_rate': 0.05735535}
INFO:tensorflow:Step 18600 per-step time 0.642s
I0123 12:28:08.325004 139706229311360 model_lib_v2.py:707] Step 18600 per-step time 0.642s
INFO:tensorflow:{'Loss/classification_loss': 0.38051832,
'Loss/localization_loss': 0.032725073,
'Loss/regularization_loss': 0.094505474,
'Loss/total_loss': 0.50774884,
'learning_rate': 0.057123937}
I0123 12:28:08.325269 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.38051832,
'Loss/localization_loss': 0.032725073,
'Loss/regularization_loss': 0.094505474,
'Loss/total_loss': 0.50774884,
'learning_rate': 0.057123937}
INFO:tensorflow:Step 18700 per-step time 0.646s
I0123 12:29:12.927405 139706229311360 model_lib_v2.py:707] Step 18700 per-step time 0.646s
INFO:tensorflow:{'Loss/classification_loss': 0.27072132,
'Loss/localization_loss': 0.034180954,
'Loss/regularization_loss': 0.09444116,
'Loss/total_loss': 0.39934343,
'learning_rate': 0.056891818}
I0123 12:29:12.927690 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.27072132,
'Loss/localization_loss': 0.034180954,
'Loss/regularization_loss': 0.09444116,
'Loss/total_loss': 0.39934343,
'learning_rate': 0.056891818}
INFO:tensorflow:Step 18800 per-step time 0.645s
I0123 12:30:17.399391 139706229311360 model_lib_v2.py:707] Step 18800 per-step time 0.645s
INFO:tensorflow:{'Loss/classification_loss': 0.42730612,
'Loss/localization_loss': 0.01078451,
'Loss/regularization_loss': 0.09441476,
'Loss/total_loss': 0.5325054,
'learning_rate': 0.056659}
I0123 12:30:17.399684 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.42730612,
'Loss/localization_loss': 0.01078451,
'Loss/regularization_loss': 0.09441476,
'Loss/total_loss': 0.5325054,
'learning_rate': 0.056659}
INFO:tensorflow:Step 18900 per-step time 0.638s
I0123 12:31:21.228335 139706229311360 model_lib_v2.py:707] Step 18900 per-step time 0.638s
INFO:tensorflow:{'Loss/classification_loss': 0.33598936,
'Loss/localization_loss': 0.017465934,
'Loss/regularization_loss': 0.09439131,
'Loss/total_loss': 0.44784662,
'learning_rate': 0.056425504}
I0123 12:31:21.228605 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.33598936,
'Loss/localization_loss': 0.017465934,
'Loss/regularization_loss': 0.09439131,
'Loss/total_loss': 0.44784662,
'learning_rate': 0.056425504}
INFO:tensorflow:Step 19000 per-step time 0.642s
I0123 12:32:25.395089 139706229311360 model_lib_v2.py:707] Step 19000 per-step time 0.642s
INFO:tensorflow:{'Loss/classification_loss': 0.39468116,
'Loss/localization_loss': 0.015678056,
'Loss/regularization_loss': 0.094344735,
'Loss/total_loss': 0.50470394,
'learning_rate': 0.056191333}
I0123 12:32:25.395357 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.39468116,
'Loss/localization_loss': 0.015678056,
'Loss/regularization_loss': 0.094344735,
'Loss/total_loss': 0.50470394,
'learning_rate': 0.056191333}
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 12:32:25.492565 139706229311360 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 12:32:25.493672 139706229311360 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 12:32:25.495607 139706229311360 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 12:32:25.496440 139706229311360 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 12:32:25.498345 139706229311360 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 12:32:25.499166 139706229311360 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 12:32:25.501069 139706229311360 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 12:32:25.501894 139706229311360 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 12:32:25.503755 139706229311360 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 12:32:25.504554 139706229311360 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Step 19100 per-step time 0.646s
I0123 12:33:30.007883 139706229311360 model_lib_v2.py:707] Step 19100 per-step time 0.646s
INFO:tensorflow:{'Loss/classification_loss': 0.33192852,
'Loss/localization_loss': 0.013866619,
'Loss/regularization_loss': 0.09434637,
'Loss/total_loss': 0.44014153,
'learning_rate': 0.055956498}
I0123 12:33:30.008175 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.33192852,
'Loss/localization_loss': 0.013866619,
'Loss/regularization_loss': 0.09434637,
'Loss/total_loss': 0.44014153,
'learning_rate': 0.055956498}
INFO:tensorflow:Step 19200 per-step time 0.648s
I0123 12:34:34.781159 139706229311360 model_lib_v2.py:707] Step 19200 per-step time 0.648s
INFO:tensorflow:{'Loss/classification_loss': 0.44508803,
'Loss/localization_loss': 0.01622589,
'Loss/regularization_loss': 0.09434605,
'Loss/total_loss': 0.55565995,
'learning_rate': 0.055721}
I0123 12:34:34.781468 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.44508803,
'Loss/localization_loss': 0.01622589,
'Loss/regularization_loss': 0.09434605,
'Loss/total_loss': 0.55565995,
'learning_rate': 0.055721}
INFO:tensorflow:Step 19300 per-step time 0.645s
I0123 12:35:39.276599 139706229311360 model_lib_v2.py:707] Step 19300 per-step time 0.645s
INFO:tensorflow:{'Loss/classification_loss': 0.33601537,
'Loss/localization_loss': 0.007376912,
'Loss/regularization_loss': 0.094355084,
'Loss/total_loss': 0.43774736,
'learning_rate': 0.05548486}
I0123 12:35:39.276923 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.33601537,
'Loss/localization_loss': 0.007376912,
'Loss/regularization_loss': 0.094355084,
'Loss/total_loss': 0.43774736,
'learning_rate': 0.05548486}
INFO:tensorflow:Step 19400 per-step time 0.640s
I0123 12:36:43.295902 139706229311360 model_lib_v2.py:707] Step 19400 per-step time 0.640s
INFO:tensorflow:{'Loss/classification_loss': 0.29710406,
'Loss/localization_loss': 0.050312616,
'Loss/regularization_loss': 0.09431048,
'Loss/total_loss': 0.44172716,
'learning_rate': 0.05524808}
I0123 12:36:43.296162 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.29710406,
'Loss/localization_loss': 0.050312616,
'Loss/regularization_loss': 0.09431048,
'Loss/total_loss': 0.44172716,
'learning_rate': 0.05524808}
INFO:tensorflow:Step 19500 per-step time 0.640s
I0123 12:37:47.339904 139706229311360 model_lib_v2.py:707] Step 19500 per-step time 0.640s
INFO:tensorflow:{'Loss/classification_loss': 0.37950346,
'Loss/localization_loss': 0.013545657,
'Loss/regularization_loss': 0.09427994,
'Loss/total_loss': 0.48732907,
'learning_rate': 0.05501068}
I0123 12:37:47.340184 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.37950346,
'Loss/localization_loss': 0.013545657,
'Loss/regularization_loss': 0.09427994,
'Loss/total_loss': 0.48732907,
'learning_rate': 0.05501068}
INFO:tensorflow:Step 19600 per-step time 0.643s
I0123 12:38:51.663791 139706229311360 model_lib_v2.py:707] Step 19600 per-step time 0.643s
INFO:tensorflow:{'Loss/classification_loss': 0.37912813,
'Loss/localization_loss': 0.024981897,
'Loss/regularization_loss': 0.094227545,
'Loss/total_loss': 0.49833757,
'learning_rate': 0.054772656}
I0123 12:38:51.664058 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.37912813,
'Loss/localization_loss': 0.024981897,
'Loss/regularization_loss': 0.094227545,
'Loss/total_loss': 0.49833757,
'learning_rate': 0.054772656}
INFO:tensorflow:Step 19700 per-step time 0.639s
I0123 12:39:55.608415 139706229311360 model_lib_v2.py:707] Step 19700 per-step time 0.639s
INFO:tensorflow:{'Loss/classification_loss': 0.30867252,
'Loss/localization_loss': 0.013113073,
'Loss/regularization_loss': 0.09418946,
'Loss/total_loss': 0.41597506,
'learning_rate': 0.05453403}
I0123 12:39:55.608703 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.30867252,
'Loss/localization_loss': 0.013113073,
'Loss/regularization_loss': 0.09418946,
'Loss/total_loss': 0.41597506,
'learning_rate': 0.05453403}
INFO:tensorflow:Step 19800 per-step time 0.645s
I0123 12:41:00.107314 139706229311360 model_lib_v2.py:707] Step 19800 per-step time 0.645s
INFO:tensorflow:{'Loss/classification_loss': 0.3054294,
'Loss/localization_loss': 0.019898904,
'Loss/regularization_loss': 0.09416133,
'Loss/total_loss': 0.41948962,
'learning_rate': 0.0542948}
I0123 12:41:00.107597 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.3054294,
'Loss/localization_loss': 0.019898904,
'Loss/regularization_loss': 0.09416133,
'Loss/total_loss': 0.41948962,
'learning_rate': 0.0542948}
INFO:tensorflow:Step 19900 per-step time 0.644s
I0123 12:42:04.467776 139706229311360 model_lib_v2.py:707] Step 19900 per-step time 0.644s
INFO:tensorflow:{'Loss/classification_loss': 0.28194886,
'Loss/localization_loss': 0.012469847,
'Loss/regularization_loss': 0.09414377,
'Loss/total_loss': 0.3885625,
'learning_rate': 0.054054994}
I0123 12:42:04.468038 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.28194886,
'Loss/localization_loss': 0.012469847,
'Loss/regularization_loss': 0.09414377,
'Loss/total_loss': 0.3885625,
'learning_rate': 0.054054994}
INFO:tensorflow:Step 20000 per-step time 0.641s
I0123 12:43:08.561310 139706229311360 model_lib_v2.py:707] Step 20000 per-step time 0.641s
INFO:tensorflow:{'Loss/classification_loss': 0.3099317,
'Loss/localization_loss': 0.01792999,
'Loss/regularization_loss': 0.09412874,
'Loss/total_loss': 0.42199045,
'learning_rate': 0.0538146}
I0123 12:43:08.561619 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.3099317,
'Loss/localization_loss': 0.01792999,
'Loss/regularization_loss': 0.09412874,
'Loss/total_loss': 0.42199045,
'learning_rate': 0.0538146}
INFO:tensorflow:Step 20100 per-step time 0.645s
I0123 12:44:13.040438 139706229311360 model_lib_v2.py:707] Step 20100 per-step time 0.645s
INFO:tensorflow:{'Loss/classification_loss': 0.30708444,
'Loss/localization_loss': 0.018148903,
'Loss/regularization_loss': 0.09414736,
'Loss/total_loss': 0.4193807,
'learning_rate': 0.053573642}
I0123 12:44:13.040767 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.30708444,
'Loss/localization_loss': 0.018148903,
'Loss/regularization_loss': 0.09414736,
'Loss/total_loss': 0.4193807,
'learning_rate': 0.053573642}
INFO:tensorflow:Step 20200 per-step time 0.641s
I0123 12:45:17.139473 139706229311360 model_lib_v2.py:707] Step 20200 per-step time 0.641s
INFO:tensorflow:{'Loss/classification_loss': 0.26902854,
'Loss/localization_loss': 0.029772656,
'Loss/regularization_loss': 0.09416794,
'Loss/total_loss': 0.39296916,
'learning_rate': 0.053332124}
I0123 12:45:17.139769 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.26902854,
'Loss/localization_loss': 0.029772656,
'Loss/regularization_loss': 0.09416794,
'Loss/total_loss': 0.39296916,
'learning_rate': 0.053332124}
INFO:tensorflow:Step 20300 per-step time 0.639s
I0123 12:46:21.023640 139706229311360 model_lib_v2.py:707] Step 20300 per-step time 0.639s
INFO:tensorflow:{'Loss/classification_loss': 0.25594407,
'Loss/localization_loss': 0.01814051,
'Loss/regularization_loss': 0.09415295,
'Loss/total_loss': 0.36823753,
'learning_rate': 0.053090062}
I0123 12:46:21.023944 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.25594407,
'Loss/localization_loss': 0.01814051,
'Loss/regularization_loss': 0.09415295,
'Loss/total_loss': 0.36823753,
'learning_rate': 0.053090062}
INFO:tensorflow:Step 20400 per-step time 0.641s
I0123 12:47:25.165766 139706229311360 model_lib_v2.py:707] Step 20400 per-step time 0.641s
INFO:tensorflow:{'Loss/classification_loss': 0.26555508,
'Loss/localization_loss': 0.01717406,
'Loss/regularization_loss': 0.09409663,
'Loss/total_loss': 0.37682578,
'learning_rate': 0.05284746}
I0123 12:47:25.166047 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.26555508,
'Loss/localization_loss': 0.01717406,
'Loss/regularization_loss': 0.09409663,
'Loss/total_loss': 0.37682578,
'learning_rate': 0.05284746}
INFO:tensorflow:Step 20500 per-step time 0.639s
I0123 12:48:29.095489 139706229311360 model_lib_v2.py:707] Step 20500 per-step time 0.639s
INFO:tensorflow:{'Loss/classification_loss': 0.33731195,
'Loss/localization_loss': 0.017706105,
'Loss/regularization_loss': 0.09410284,
'Loss/total_loss': 0.44912088,
'learning_rate': 0.052604325}
I0123 12:48:29.095779 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.33731195,
'Loss/localization_loss': 0.017706105,
'Loss/regularization_loss': 0.09410284,
'Loss/total_loss': 0.44912088,
'learning_rate': 0.052604325}
INFO:tensorflow:Step 20600 per-step time 0.642s
I0123 12:49:33.294761 139706229311360 model_lib_v2.py:707] Step 20600 per-step time 0.642s
INFO:tensorflow:{'Loss/classification_loss': 0.29128116,
'Loss/localization_loss': 0.018047579,
'Loss/regularization_loss': 0.09404613,
'Loss/total_loss': 0.40337485,
'learning_rate': 0.052360676}
I0123 12:49:33.295041 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.29128116,
'Loss/localization_loss': 0.018047579,
'Loss/regularization_loss': 0.09404613,
'Loss/total_loss': 0.40337485,
'learning_rate': 0.052360676}
INFO:tensorflow:Step 20700 per-step time 0.645s
I0123 12:50:37.771266 139706229311360 model_lib_v2.py:707] Step 20700 per-step time 0.645s
INFO:tensorflow:{'Loss/classification_loss': 0.27465734,
'Loss/localization_loss': 0.043579392,
'Loss/regularization_loss': 0.094031185,
'Loss/total_loss': 0.41226792,
'learning_rate': 0.05211652}
I0123 12:50:37.771540 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.27465734,
'Loss/localization_loss': 0.043579392,
'Loss/regularization_loss': 0.094031185,
'Loss/total_loss': 0.41226792,
'learning_rate': 0.05211652}
INFO:tensorflow:Step 20800 per-step time 0.643s
I0123 12:51:42.085655 139706229311360 model_lib_v2.py:707] Step 20800 per-step time 0.643s
INFO:tensorflow:{'Loss/classification_loss': 0.3206833,
'Loss/localization_loss': 0.012931333,
'Loss/regularization_loss': 0.09398094,
'Loss/total_loss': 0.4275956,
'learning_rate': 0.05187187}
I0123 12:51:42.085958 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.3206833,
'Loss/localization_loss': 0.012931333,
'Loss/regularization_loss': 0.09398094,
'Loss/total_loss': 0.4275956,
'learning_rate': 0.05187187}
INFO:tensorflow:Step 20900 per-step time 0.642s
I0123 12:52:46.293357 139706229311360 model_lib_v2.py:707] Step 20900 per-step time 0.642s
INFO:tensorflow:{'Loss/classification_loss': 0.29483506,
'Loss/localization_loss': 0.011464682,
'Loss/regularization_loss': 0.09392528,
'Loss/total_loss': 0.40022504,
'learning_rate': 0.051626723}
I0123 12:52:46.293627 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.29483506,
'Loss/localization_loss': 0.011464682,
'Loss/regularization_loss': 0.09392528,
'Loss/total_loss': 0.40022504,
'learning_rate': 0.051626723}
INFO:tensorflow:Step 21000 per-step time 0.640s
I0123 12:53:50.312822 139706229311360 model_lib_v2.py:707] Step 21000 per-step time 0.640s
INFO:tensorflow:{'Loss/classification_loss': 0.33710402,
'Loss/localization_loss': 0.031402607,
'Loss/regularization_loss': 0.09388614,
'Loss/total_loss': 0.46239278,
'learning_rate': 0.0513811}
I0123 12:53:50.313095 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.33710402,
'Loss/localization_loss': 0.031402607,
'Loss/regularization_loss': 0.09388614,
'Loss/total_loss': 0.46239278,
'learning_rate': 0.0513811}
INFO:tensorflow:Step 21100 per-step time 0.642s
I0123 12:54:54.564421 139706229311360 model_lib_v2.py:707] Step 21100 per-step time 0.642s
INFO:tensorflow:{'Loss/classification_loss': 0.29582432,
'Loss/localization_loss': 0.017465543,
'Loss/regularization_loss': 0.09386003,
'Loss/total_loss': 0.40714988,
'learning_rate': 0.051135015}
I0123 12:54:54.564747 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.29582432,
'Loss/localization_loss': 0.017465543,
'Loss/regularization_loss': 0.09386003,
'Loss/total_loss': 0.40714988,
'learning_rate': 0.051135015}
INFO:tensorflow:Step 21200 per-step time 0.646s
I0123 12:55:59.129917 139706229311360 model_lib_v2.py:707] Step 21200 per-step time 0.646s
INFO:tensorflow:{'Loss/classification_loss': 0.31103155,
'Loss/localization_loss': 0.053711094,
'Loss/regularization_loss': 0.09383644,
'Loss/total_loss': 0.45857906,
'learning_rate': 0.050888464}
I0123 12:55:59.130260 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.31103155,
'Loss/localization_loss': 0.053711094,
'Loss/regularization_loss': 0.09383644,
'Loss/total_loss': 0.45857906,
'learning_rate': 0.050888464}
INFO:tensorflow:Step 21300 per-step time 0.645s
I0123 12:57:03.620060 139706229311360 model_lib_v2.py:707] Step 21300 per-step time 0.645s
INFO:tensorflow:{'Loss/classification_loss': 0.2628134,
'Loss/localization_loss': 0.011737872,
'Loss/regularization_loss': 0.093829274,
'Loss/total_loss': 0.36838055,
'learning_rate': 0.05064147}
I0123 12:57:03.620352 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.2628134,
'Loss/localization_loss': 0.011737872,
'Loss/regularization_loss': 0.093829274,
'Loss/total_loss': 0.36838055,
'learning_rate': 0.05064147}
INFO:tensorflow:Step 21400 per-step time 0.644s
I0123 12:58:08.039330 139706229311360 model_lib_v2.py:707] Step 21400 per-step time 0.644s
INFO:tensorflow:{'Loss/classification_loss': 0.39088708,
'Loss/localization_loss': 0.00788187,
'Loss/regularization_loss': 0.09382318,
'Loss/total_loss': 0.49259216,
'learning_rate': 0.05039404}
I0123 12:58:08.039595 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.39088708,
'Loss/localization_loss': 0.00788187,
'Loss/regularization_loss': 0.09382318,
'Loss/total_loss': 0.49259216,
'learning_rate': 0.05039404}
INFO:tensorflow:Step 21500 per-step time 0.643s
I0123 12:59:12.354693 139706229311360 model_lib_v2.py:707] Step 21500 per-step time 0.643s
INFO:tensorflow:{'Loss/classification_loss': 0.3578997,
'Loss/localization_loss': 0.021961076,
'Loss/regularization_loss': 0.093807176,
'Loss/total_loss': 0.47366792,
'learning_rate': 0.050146177}
I0123 12:59:12.354980 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.3578997,
'Loss/localization_loss': 0.021961076,
'Loss/regularization_loss': 0.093807176,
'Loss/total_loss': 0.47366792,
'learning_rate': 0.050146177}
INFO:tensorflow:Step 21600 per-step time 0.641s
I0123 13:00:16.424781 139706229311360 model_lib_v2.py:707] Step 21600 per-step time 0.641s
INFO:tensorflow:{'Loss/classification_loss': 0.4160522,
'Loss/localization_loss': 0.025698824,
'Loss/regularization_loss': 0.093816385,
'Loss/total_loss': 0.5355674,
'learning_rate': 0.04989791}
I0123 13:00:16.425070 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.4160522,
'Loss/localization_loss': 0.025698824,
'Loss/regularization_loss': 0.093816385,
'Loss/total_loss': 0.5355674,
'learning_rate': 0.04989791}
INFO:tensorflow:Step 21700 per-step time 0.643s
I0123 13:01:20.713320 139706229311360 model_lib_v2.py:707] Step 21700 per-step time 0.643s
INFO:tensorflow:{'Loss/classification_loss': 0.25954783,
'Loss/localization_loss': 0.0057919053,
'Loss/regularization_loss': 0.093752325,
'Loss/total_loss': 0.35909206,
'learning_rate': 0.049649224}
I0123 13:01:20.713607 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.25954783,
'Loss/localization_loss': 0.0057919053,
'Loss/regularization_loss': 0.093752325,
'Loss/total_loss': 0.35909206,
'learning_rate': 0.049649224}
INFO:tensorflow:Step 21800 per-step time 0.640s
I0123 13:02:24.758477 139706229311360 model_lib_v2.py:707] Step 21800 per-step time 0.640s
INFO:tensorflow:{'Loss/classification_loss': 0.2827692,
'Loss/localization_loss': 0.020943783,
'Loss/regularization_loss': 0.09374389,
'Loss/total_loss': 0.39745688,
'learning_rate': 0.049400143}
I0123 13:02:24.758762 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.2827692,
'Loss/localization_loss': 0.020943783,
'Loss/regularization_loss': 0.09374389,
'Loss/total_loss': 0.39745688,
'learning_rate': 0.049400143}
INFO:tensorflow:Step 21900 per-step time 0.641s
I0123 13:03:28.876546 139706229311360 model_lib_v2.py:707] Step 21900 per-step time 0.641s
INFO:tensorflow:{'Loss/classification_loss': 0.21058424,
'Loss/localization_loss': 0.014391282,
'Loss/regularization_loss': 0.09373436,
'Loss/total_loss': 0.31870988,
'learning_rate': 0.049150676}
I0123 13:03:28.876828 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.21058424,
'Loss/localization_loss': 0.014391282,
'Loss/regularization_loss': 0.09373436,
'Loss/total_loss': 0.31870988,
'learning_rate': 0.049150676}
INFO:tensorflow:Step 22000 per-step time 0.639s
I0123 13:04:32.799571 139706229311360 model_lib_v2.py:707] Step 22000 per-step time 0.639s
INFO:tensorflow:{'Loss/classification_loss': 0.21170788,
'Loss/localization_loss': 0.013087756,
'Loss/regularization_loss': 0.09367338,
'Loss/total_loss': 0.318469,
'learning_rate': 0.048900835}
I0123 13:04:32.799846 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.21170788,
'Loss/localization_loss': 0.013087756,
'Loss/regularization_loss': 0.09367338,
'Loss/total_loss': 0.318469,
'learning_rate': 0.048900835}
INFO:tensorflow:Step 22100 per-step time 0.647s
I0123 13:05:37.464426 139706229311360 model_lib_v2.py:707] Step 22100 per-step time 0.647s
INFO:tensorflow:{'Loss/classification_loss': 0.36087686,
'Loss/localization_loss': 0.014638118,
'Loss/regularization_loss': 0.093622334,
'Loss/total_loss': 0.4691373,
'learning_rate': 0.048650626}
I0123 13:05:37.464746 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.36087686,
'Loss/localization_loss': 0.014638118,
'Loss/regularization_loss': 0.093622334,
'Loss/total_loss': 0.4691373,
'learning_rate': 0.048650626}
INFO:tensorflow:Step 22200 per-step time 0.645s
I0123 13:06:41.950815 139706229311360 model_lib_v2.py:707] Step 22200 per-step time 0.645s
INFO:tensorflow:{'Loss/classification_loss': 0.29098597,
'Loss/localization_loss': 0.022130178,
'Loss/regularization_loss': 0.09361759,
'Loss/total_loss': 0.40673375,
'learning_rate': 0.048400067}
I0123 13:06:41.951084 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.29098597,
'Loss/localization_loss': 0.022130178,
'Loss/regularization_loss': 0.09361759,
'Loss/total_loss': 0.40673375,
'learning_rate': 0.048400067}
INFO:tensorflow:Step 22300 per-step time 0.644s
I0123 13:07:46.309553 139706229311360 model_lib_v2.py:707] Step 22300 per-step time 0.644s
INFO:tensorflow:{'Loss/classification_loss': 0.4057059,
'Loss/localization_loss': 0.012178864,
'Loss/regularization_loss': 0.093603,
'Loss/total_loss': 0.5114878,
'learning_rate': 0.048149157}
I0123 13:07:46.309843 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.4057059,
'Loss/localization_loss': 0.012178864,
'Loss/regularization_loss': 0.093603,
'Loss/total_loss': 0.5114878,
'learning_rate': 0.048149157}
INFO:tensorflow:Step 22400 per-step time 0.639s
I0123 13:08:50.202278 139706229311360 model_lib_v2.py:707] Step 22400 per-step time 0.639s
INFO:tensorflow:{'Loss/classification_loss': 0.32117268,
'Loss/localization_loss': 0.013533677,
'Loss/regularization_loss': 0.09357316,
'Loss/total_loss': 0.42827952,
'learning_rate': 0.04789791}
I0123 13:08:50.202545 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.32117268,
'Loss/localization_loss': 0.013533677,
'Loss/regularization_loss': 0.09357316,
'Loss/total_loss': 0.42827952,
'learning_rate': 0.04789791}
INFO:tensorflow:Step 22500 per-step time 0.643s
I0123 13:09:54.519349 139706229311360 model_lib_v2.py:707] Step 22500 per-step time 0.643s
INFO:tensorflow:{'Loss/classification_loss': 0.33383873,
'Loss/localization_loss': 0.017355572,
'Loss/regularization_loss': 0.09352026,
'Loss/total_loss': 0.44471455,
'learning_rate': 0.04764634}
I0123 13:09:54.519620 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.33383873,
'Loss/localization_loss': 0.017355572,
'Loss/regularization_loss': 0.09352026,
'Loss/total_loss': 0.44471455,
'learning_rate': 0.04764634}
INFO:tensorflow:Step 22600 per-step time 0.646s
I0123 13:10:59.106294 139706229311360 model_lib_v2.py:707] Step 22600 per-step time 0.646s
INFO:tensorflow:{'Loss/classification_loss': 0.27428612,
'Loss/localization_loss': 0.015689908,
'Loss/regularization_loss': 0.09347234,
'Loss/total_loss': 0.38344836,
'learning_rate': 0.047394462}
I0123 13:10:59.106573 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.27428612,
'Loss/localization_loss': 0.015689908,
'Loss/regularization_loss': 0.09347234,
'Loss/total_loss': 0.38344836,
'learning_rate': 0.047394462}
INFO:tensorflow:Step 22700 per-step time 0.642s
I0123 13:12:03.260778 139706229311360 model_lib_v2.py:707] Step 22700 per-step time 0.642s
INFO:tensorflow:{'Loss/classification_loss': 0.31313714,
'Loss/localization_loss': 0.025391534,
'Loss/regularization_loss': 0.09344882,
'Loss/total_loss': 0.4319775,
'learning_rate': 0.04714227}
I0123 13:12:03.261055 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.31313714,
'Loss/localization_loss': 0.025391534,
'Loss/regularization_loss': 0.09344882,
'Loss/total_loss': 0.4319775,
'learning_rate': 0.04714227}
INFO:tensorflow:Step 22800 per-step time 0.644s
I0123 13:13:07.619523 139706229311360 model_lib_v2.py:707] Step 22800 per-step time 0.644s
INFO:tensorflow:{'Loss/classification_loss': 0.30770117,
'Loss/localization_loss': 0.008426239,
'Loss/regularization_loss': 0.093440436,
'Loss/total_loss': 0.40956786,
'learning_rate': 0.046889797}
I0123 13:13:07.619822 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.30770117,
'Loss/localization_loss': 0.008426239,
'Loss/regularization_loss': 0.093440436,
'Loss/total_loss': 0.40956786,
'learning_rate': 0.046889797}
INFO:tensorflow:Step 22900 per-step time 0.645s
I0123 13:14:12.153773 139706229311360 model_lib_v2.py:707] Step 22900 per-step time 0.645s
INFO:tensorflow:{'Loss/classification_loss': 0.31052658,
'Loss/localization_loss': 0.040365666,
'Loss/regularization_loss': 0.09340423,
'Loss/total_loss': 0.44429648,
'learning_rate': 0.046637025}
I0123 13:14:12.154053 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.31052658,
'Loss/localization_loss': 0.040365666,
'Loss/regularization_loss': 0.09340423,
'Loss/total_loss': 0.44429648,
'learning_rate': 0.046637025}
INFO:tensorflow:Step 23000 per-step time 0.643s
I0123 13:15:16.458415 139706229311360 model_lib_v2.py:707] Step 23000 per-step time 0.643s
INFO:tensorflow:{'Loss/classification_loss': 0.24036367,
'Loss/localization_loss': 0.024973867,
'Loss/regularization_loss': 0.09338324,
'Loss/total_loss': 0.35872078,
'learning_rate': 0.046383996}
I0123 13:15:16.458698 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.24036367,
'Loss/localization_loss': 0.024973867,
'Loss/regularization_loss': 0.09338324,
'Loss/total_loss': 0.35872078,
'learning_rate': 0.046383996}
INFO:tensorflow:Step 23100 per-step time 0.648s
I0123 13:16:21.227266 139706229311360 model_lib_v2.py:707] Step 23100 per-step time 0.648s
INFO:tensorflow:{'Loss/classification_loss': 0.30117485,
'Loss/localization_loss': 0.022079531,
'Loss/regularization_loss': 0.093348145,
'Loss/total_loss': 0.41660252,
'learning_rate': 0.04613069}
I0123 13:16:21.227533 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.30117485,
'Loss/localization_loss': 0.022079531,
'Loss/regularization_loss': 0.093348145,
'Loss/total_loss': 0.41660252,
'learning_rate': 0.04613069}
INFO:tensorflow:Step 23200 per-step time 0.644s
I0123 13:17:25.631228 139706229311360 model_lib_v2.py:707] Step 23200 per-step time 0.644s
INFO:tensorflow:{'Loss/classification_loss': 0.25450078,
'Loss/localization_loss': 0.011690955,
'Loss/regularization_loss': 0.09329549,
'Loss/total_loss': 0.3594872,
'learning_rate': 0.04587714}
I0123 13:17:25.631542 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.25450078,
'Loss/localization_loss': 0.011690955,
'Loss/regularization_loss': 0.09329549,
'Loss/total_loss': 0.3594872,
'learning_rate': 0.04587714}
INFO:tensorflow:Step 23300 per-step time 0.641s
I0123 13:18:29.705097 139706229311360 model_lib_v2.py:707] Step 23300 per-step time 0.641s
INFO:tensorflow:{'Loss/classification_loss': 0.2776896,
'Loss/localization_loss': 0.014632729,
'Loss/regularization_loss': 0.09325454,
'Loss/total_loss': 0.38557687,
'learning_rate': 0.045623355}
I0123 13:18:29.705380 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.2776896,
'Loss/localization_loss': 0.014632729,
'Loss/regularization_loss': 0.09325454,
'Loss/total_loss': 0.38557687,
'learning_rate': 0.045623355}
INFO:tensorflow:Step 23400 per-step time 0.646s
I0123 13:19:34.271421 139706229311360 model_lib_v2.py:707] Step 23400 per-step time 0.646s
INFO:tensorflow:{'Loss/classification_loss': 0.24592155,
'Loss/localization_loss': 0.013863071,
'Loss/regularization_loss': 0.09324024,
'Loss/total_loss': 0.35302484,
'learning_rate': 0.045369323}
I0123 13:19:34.271768 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.24592155,
'Loss/localization_loss': 0.013863071,
'Loss/regularization_loss': 0.09324024,
'Loss/total_loss': 0.35302484,
'learning_rate': 0.045369323}
INFO:tensorflow:Step 23500 per-step time 0.643s
I0123 13:20:38.538082 139706229311360 model_lib_v2.py:707] Step 23500 per-step time 0.643s
INFO:tensorflow:{'Loss/classification_loss': 0.22123255,
'Loss/localization_loss': 0.010604003,
'Loss/regularization_loss': 0.09323239,
'Loss/total_loss': 0.32506895,
'learning_rate': 0.045115083}
I0123 13:20:38.538361 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.22123255,
'Loss/localization_loss': 0.010604003,
'Loss/regularization_loss': 0.09323239,
'Loss/total_loss': 0.32506895,
'learning_rate': 0.045115083}
INFO:tensorflow:Step 23600 per-step time 0.645s
I0123 13:21:42.989419 139706229311360 model_lib_v2.py:707] Step 23600 per-step time 0.645s
INFO:tensorflow:{'Loss/classification_loss': 0.2729299,
'Loss/localization_loss': 0.015970767,
'Loss/regularization_loss': 0.09321229,
'Loss/total_loss': 0.38211298,
'learning_rate': 0.044860635}
I0123 13:21:42.989691 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.2729299,
'Loss/localization_loss': 0.015970767,
'Loss/regularization_loss': 0.09321229,
'Loss/total_loss': 0.38211298,
'learning_rate': 0.044860635}
INFO:tensorflow:Step 23700 per-step time 0.644s
I0123 13:22:47.412185 139706229311360 model_lib_v2.py:707] Step 23700 per-step time 0.644s
INFO:tensorflow:{'Loss/classification_loss': 0.24451514,
'Loss/localization_loss': 0.02105424,
'Loss/regularization_loss': 0.09319431,
'Loss/total_loss': 0.3587637,
'learning_rate': 0.044605974}
I0123 13:22:47.412456 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.24451514,
'Loss/localization_loss': 0.02105424,
'Loss/regularization_loss': 0.09319431,
'Loss/total_loss': 0.3587637,
'learning_rate': 0.044605974}
INFO:tensorflow:Step 23800 per-step time 0.641s
I0123 13:23:51.530093 139706229311360 model_lib_v2.py:707] Step 23800 per-step time 0.641s
INFO:tensorflow:{'Loss/classification_loss': 0.25716016,
'Loss/localization_loss': 0.012039635,
'Loss/regularization_loss': 0.09315794,
'Loss/total_loss': 0.36235774,
'learning_rate': 0.044351134}
I0123 13:23:51.530377 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.25716016,
'Loss/localization_loss': 0.012039635,
'Loss/regularization_loss': 0.09315794,
'Loss/total_loss': 0.36235774,
'learning_rate': 0.044351134}
INFO:tensorflow:Step 23900 per-step time 0.644s
I0123 13:24:55.941889 139706229311360 model_lib_v2.py:707] Step 23900 per-step time 0.644s
INFO:tensorflow:{'Loss/classification_loss': 0.32431552,
'Loss/localization_loss': 0.029292773,
'Loss/regularization_loss': 0.09310538,
'Loss/total_loss': 0.44671366,
'learning_rate': 0.044096105}
I0123 13:24:55.942199 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.32431552,
'Loss/localization_loss': 0.029292773,
'Loss/regularization_loss': 0.09310538,
'Loss/total_loss': 0.44671366,
'learning_rate': 0.044096105}
INFO:tensorflow:Step 24000 per-step time 0.645s
I0123 13:26:00.485518 139706229311360 model_lib_v2.py:707] Step 24000 per-step time 0.645s
INFO:tensorflow:{'Loss/classification_loss': 0.29332164,
'Loss/localization_loss': 0.011581618,
'Loss/regularization_loss': 0.093092926,
'Loss/total_loss': 0.3979962,
'learning_rate': 0.043840922}
I0123 13:26:00.485812 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.29332164,
'Loss/localization_loss': 0.011581618,
'Loss/regularization_loss': 0.093092926,
'Loss/total_loss': 0.3979962,
'learning_rate': 0.043840922}
INFO:tensorflow:Step 24100 per-step time 0.651s
I0123 13:27:05.567079 139706229311360 model_lib_v2.py:707] Step 24100 per-step time 0.651s
INFO:tensorflow:{'Loss/classification_loss': 0.2538514,
'Loss/localization_loss': 0.014870389,
'Loss/regularization_loss': 0.09308598,
'Loss/total_loss': 0.36180776,
'learning_rate': 0.043585572}
I0123 13:27:05.567408 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.2538514,
'Loss/localization_loss': 0.014870389,
'Loss/regularization_loss': 0.09308598,
'Loss/total_loss': 0.36180776,
'learning_rate': 0.043585572}
INFO:tensorflow:Step 24200 per-step time 0.641s
I0123 13:28:09.635822 139706229311360 model_lib_v2.py:707] Step 24200 per-step time 0.641s
INFO:tensorflow:{'Loss/classification_loss': 0.2608899,
'Loss/localization_loss': 0.016551472,
'Loss/regularization_loss': 0.09304116,
'Loss/total_loss': 0.3704825,
'learning_rate': 0.043330073}
I0123 13:28:09.636123 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.2608899,
'Loss/localization_loss': 0.016551472,
'Loss/regularization_loss': 0.09304116,
'Loss/total_loss': 0.3704825,
'learning_rate': 0.043330073}
INFO:tensorflow:Step 24300 per-step time 0.645s
I0123 13:29:14.163892 139706229311360 model_lib_v2.py:707] Step 24300 per-step time 0.645s
INFO:tensorflow:{'Loss/classification_loss': 0.30597606,
'Loss/localization_loss': 0.018280154,
'Loss/regularization_loss': 0.09300857,
'Loss/total_loss': 0.4172648,
'learning_rate': 0.04307444}
I0123 13:29:14.164170 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.30597606,
'Loss/localization_loss': 0.018280154,
'Loss/regularization_loss': 0.09300857,
'Loss/total_loss': 0.4172648,
'learning_rate': 0.04307444}
INFO:tensorflow:Step 24400 per-step time 0.642s
I0123 13:30:18.377874 139706229311360 model_lib_v2.py:707] Step 24400 per-step time 0.642s
INFO:tensorflow:{'Loss/classification_loss': 0.21212146,
'Loss/localization_loss': 0.013448623,
'Loss/regularization_loss': 0.09298284,
'Loss/total_loss': 0.3185529,
'learning_rate': 0.04281868}
I0123 13:30:18.378145 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.21212146,
'Loss/localization_loss': 0.013448623,
'Loss/regularization_loss': 0.09298284,
'Loss/total_loss': 0.3185529,
'learning_rate': 0.04281868}
INFO:tensorflow:Step 24500 per-step time 0.641s
I0123 13:31:22.484993 139706229311360 model_lib_v2.py:707] Step 24500 per-step time 0.641s
INFO:tensorflow:{'Loss/classification_loss': 0.2651904,
'Loss/localization_loss': 0.013821077,
'Loss/regularization_loss': 0.0929415,
'Loss/total_loss': 0.37195295,
'learning_rate': 0.042562805}
I0123 13:31:22.485275 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.2651904,
'Loss/localization_loss': 0.013821077,
'Loss/regularization_loss': 0.0929415,
'Loss/total_loss': 0.37195295,
'learning_rate': 0.042562805}
INFO:tensorflow:Step 24600 per-step time 0.643s
I0123 13:32:26.764012 139706229311360 model_lib_v2.py:707] Step 24600 per-step time 0.643s
INFO:tensorflow:{'Loss/classification_loss': 0.2554271,
'Loss/localization_loss': 0.017306734,
'Loss/regularization_loss': 0.09291727,
'Loss/total_loss': 0.3656511,
'learning_rate': 0.04230683}
I0123 13:32:26.764289 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.2554271,
'Loss/localization_loss': 0.017306734,
'Loss/regularization_loss': 0.09291727,
'Loss/total_loss': 0.3656511,
'learning_rate': 0.04230683}
INFO:tensorflow:Step 24700 per-step time 0.642s
I0123 13:33:30.963247 139706229311360 model_lib_v2.py:707] Step 24700 per-step time 0.642s
INFO:tensorflow:{'Loss/classification_loss': 0.38368958,
'Loss/localization_loss': 0.015524857,
'Loss/regularization_loss': 0.09286607,
'Loss/total_loss': 0.4920805,
'learning_rate': 0.042050745}
I0123 13:33:30.963555 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.38368958,
'Loss/localization_loss': 0.015524857,
'Loss/regularization_loss': 0.09286607,
'Loss/total_loss': 0.4920805,
'learning_rate': 0.042050745}
INFO:tensorflow:Step 24800 per-step time 0.644s
I0123 13:34:35.318193 139706229311360 model_lib_v2.py:707] Step 24800 per-step time 0.644s
INFO:tensorflow:{'Loss/classification_loss': 0.3103263,
'Loss/localization_loss': 0.010406563,
'Loss/regularization_loss': 0.09281407,
'Loss/total_loss': 0.41354692,
'learning_rate': 0.04179459}
I0123 13:34:35.318495 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.3103263,
'Loss/localization_loss': 0.010406563,
'Loss/regularization_loss': 0.09281407,
'Loss/total_loss': 0.41354692,
'learning_rate': 0.04179459}
INFO:tensorflow:Step 24900 per-step time 0.644s
I0123 13:35:39.735392 139706229311360 model_lib_v2.py:707] Step 24900 per-step time 0.644s
INFO:tensorflow:{'Loss/classification_loss': 0.24705991,
'Loss/localization_loss': 0.018297436,
'Loss/regularization_loss': 0.09276485,
'Loss/total_loss': 0.3581222,
'learning_rate': 0.04153835}
I0123 13:35:39.735681 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.24705991,
'Loss/localization_loss': 0.018297436,
'Loss/regularization_loss': 0.09276485,
'Loss/total_loss': 0.3581222,
'learning_rate': 0.04153835}
INFO:tensorflow:Step 25000 per-step time 0.638s
I0123 13:36:43.587889 139706229311360 model_lib_v2.py:707] Step 25000 per-step time 0.638s
INFO:tensorflow:{'Loss/classification_loss': 0.24062178,
'Loss/localization_loss': 0.016733974,
'Loss/regularization_loss': 0.09272963,
'Loss/total_loss': 0.35008538,
'learning_rate': 0.04128206}
I0123 13:36:43.588164 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.24062178,
'Loss/localization_loss': 0.016733974,
'Loss/regularization_loss': 0.09272963,
'Loss/total_loss': 0.35008538,
'learning_rate': 0.04128206}
INFO:tensorflow:Step 25100 per-step time 0.647s
I0123 13:37:48.273289 139706229311360 model_lib_v2.py:707] Step 25100 per-step time 0.647s
INFO:tensorflow:{'Loss/classification_loss': 0.21411954,
'Loss/localization_loss': 0.020920731,
'Loss/regularization_loss': 0.09270904,
'Loss/total_loss': 0.3277493,
'learning_rate': 0.041025713}
I0123 13:37:48.273579 139706229311360 model_lib_v2.py:708] {'Loss/classification_loss': 0.21411954,
'Loss/localization_loss': 0.020920731,
'Loss/regularization_loss': 0.09270904,
'Loss/total_loss': 0.3277493,
'learning_rate': 0.041025713}
Traceback (most recent call last):
File "model_main_tf2.py", line 115, in <module>
tf.compat.v1.app.run()
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/platform/app.py", line 40, in run
_run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
File "/usr/local/lib/python3.7/dist-packages/absl/app.py", line 303, in run
_run_main(main, args)
File "/usr/local/lib/python3.7/dist-packages/absl/app.py", line 251, in _run_main
sys.exit(main(argv))
File "model_main_tf2.py", line 112, in main
record_summaries=FLAGS.record_summaries)
File "/usr/local/lib/python3.7/dist-packages/object_detection/model_lib_v2.py", line 685, in train_loop
losses_dict = _dist_train_step(train_input_iter)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/traceback_utils.py", line 150, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py", line 910, in __call__
result = self._call(*args, **kwds)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py", line 942, in _call
return self._stateless_fn(*args, **kwds) # pylint: disable=not-callable
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py", line 3131, in __call__
filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py", line 1960, in _call_flat
ctx, args, cancellation_manager=cancellation_manager))
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py", line 603, in call
ctx=ctx)
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py", line 59, in quick_execute
inputs, attrs, num_outputs)
KeyboardInterrupt
!python model_main_tf2.py --model_dir=models/my_ssd_mobilenet --pipeline_config_path=models/my_ssd_mobilenet/pipeline.config --checkpoint_dir=models/my_ssd_mobilenet
Traceback (most recent call last):
File "model_main_tf2.py", line 32, in <module>
from object_detection import model_lib_v2
ModuleNotFoundError: No module named 'object_detection'
!pwd
/content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd
!python ./exporter_main_v2.py --input_type image_tensor --pipeline_config_path ./models/my_ssd_mobilenet/pipeline.config --trained_checkpoint_dir ./models/my_ssd_mobilenet/ --output_directory ./exported-models/my_model
2022-01-23 13:47:36.848232: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0. WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:464: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version. Instructions for updating: back_prop=False is deprecated. Consider using tf.stop_gradient instead. Instead of: results = tf.map_fn(fn, elems, back_prop=False) Use: results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems)) W0123 13:47:37.120375 139807483082624 deprecation.py:619] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:464: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version. Instructions for updating: back_prop=False is deprecated. Consider using tf.stop_gradient instead. Instead of: results = tf.map_fn(fn, elems, back_prop=False) Use: results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems)) WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7f26c0481790>, because it is not built. W0123 13:48:40.042584 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7f26c0481790>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.convolutional.SeparableConv2D object at 0x7f26c0332cd0>, because it is not built. W0123 13:48:40.180683 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.convolutional.SeparableConv2D object at 0x7f26c0332cd0>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aa7c4d0>, because it is not built. W0123 13:48:40.180946 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aa7c4d0>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263abc3fd0>, because it is not built. W0123 13:48:40.181043 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263abc3fd0>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.convolutional.SeparableConv2D object at 0x7f263ae03c90>, because it is not built. W0123 13:48:40.181126 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.convolutional.SeparableConv2D object at 0x7f263ae03c90>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aaa0790>, because it is not built. W0123 13:48:40.181198 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aaa0790>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aaa0d10>, because it is not built. W0123 13:48:40.181265 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aaa0d10>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.convolutional.SeparableConv2D object at 0x7f26c00ddc10>, because it is not built. W0123 13:48:40.181330 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.convolutional.SeparableConv2D object at 0x7f26c00ddc10>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aaa03d0>, because it is not built. W0123 13:48:40.181404 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aaa03d0>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263ac446d0>, because it is not built. W0123 13:48:40.181459 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263ac446d0>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.convolutional.SeparableConv2D object at 0x7f263ac44f10>, because it is not built. W0123 13:48:40.181513 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.convolutional.SeparableConv2D object at 0x7f263ac44f10>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f26521cef90>, because it is not built. W0123 13:48:40.181567 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f26521cef90>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263ac74990>, because it is not built. W0123 13:48:40.181622 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263ac74990>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f26c0339a90>, because it is not built. W0123 13:48:40.181675 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f26c0339a90>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa21150>, because it is not built. W0123 13:48:40.181739 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa21150>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aa219d0>, because it is not built. W0123 13:48:40.181796 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aa219d0>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa21f10>, because it is not built. W0123 13:48:40.181851 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa21f10>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ab22890>, because it is not built. W0123 13:48:40.181906 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ab22890>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa1b410>, because it is not built. W0123 13:48:40.181965 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa1b410>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aa1b390>, because it is not built. W0123 13:48:40.182022 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aa1b390>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa1bc10>, because it is not built. W0123 13:48:40.182077 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa1bc10>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f26c0339ad0>, because it is not built. W0123 13:48:40.182204 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f26c0339ad0>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa15ed0>, because it is not built. W0123 13:48:40.182262 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa15ed0>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ac5dcd0>, because it is not built. W0123 13:48:40.182318 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ac5dcd0>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa2b790>, because it is not built. W0123 13:48:40.182373 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa2b790>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ab47790>, because it is not built. W0123 13:48:40.182427 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ab47790>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa2bb90>, because it is not built. W0123 13:48:40.182485 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aa2bb90>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aa2bc50>, because it is not built. W0123 13:48:40.182540 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aa2bc50>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aaad1d0>, because it is not built. W0123 13:48:40.182594 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263aaad1d0>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ab1aa10>, because it is not built. W0123 13:48:40.182648 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ab1aa10>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263ae64b50>, because it is not built. W0123 13:48:40.182706 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263ae64b50>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263a97bcd0>, because it is not built. W0123 13:48:40.182767 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263a97bcd0>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263ab5fad0>, because it is not built. W0123 13:48:40.182822 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263ab5fad0>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ac5d110>, because it is not built. W0123 13:48:40.182875 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ac5d110>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263a9b6610>, because it is not built. W0123 13:48:40.182928 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263a9b6610>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ac44250>, because it is not built. W0123 13:48:40.182985 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ac44250>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263a9b6850>, because it is not built. W0123 13:48:40.183042 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263a9b6850>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f26c0339b10>, because it is not built. W0123 13:48:40.183103 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f26c0339b10>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263a99f6d0>, because it is not built. W0123 13:48:40.183159 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263a99f6d0>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aaa0490>, because it is not built. W0123 13:48:40.183212 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263aaa0490>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263a9400d0>, because it is not built. W0123 13:48:40.183266 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263a9400d0>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263a940a50>, because it is not built. W0123 13:48:40.183320 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263a940a50>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263a940c50>, because it is not built. W0123 13:48:40.183383 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263a940c50>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ae222d0>, because it is not built. W0123 13:48:40.183437 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f263ae222d0>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263a940e10>, because it is not built. W0123 13:48:40.232452 139807483082624 save_impl.py:72] Skipping full serialization of Keras layer <keras.layers.core.lambda_layer.Lambda object at 0x7f263a940e10>, because it is not built. 2022-01-23 13:48:49.249368: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them. W0123 13:49:27.565280 139807483082624 save.py:268] Found untraced functions such as WeightSharedConvolutionalBoxPredictor_layer_call_fn, WeightSharedConvolutionalBoxPredictor_layer_call_and_return_conditional_losses, WeightSharedConvolutionalBoxHead_layer_call_fn, WeightSharedConvolutionalBoxHead_layer_call_and_return_conditional_losses, WeightSharedConvolutionalBoxPredictor_layer_call_fn while saving (showing 5 of 260). These functions will not be directly callable after loading. INFO:tensorflow:Assets written to: ./exported-models/my_model/saved_model/assets I0123 13:49:36.735169 139807483082624 builder_impl.py:784] Assets written to: ./exported-models/my_model/saved_model/assets INFO:tensorflow:Writing pipeline config file to ./exported-models/my_model/pipeline.config I0123 13:49:37.740982 139807483082624 config_util.py:254] Writing pipeline config file to ./exported-models/my_model/pipeline.config
!python model_main_tf2.py --model_dir=models/my_rcnn_imagenet --pipeline_config_path=models/my_rcnn_imagenet/pipeline.config
2022-01-23 13:57:12.252312: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
I0123 13:57:12.255908 140287364614016 mirrored_strategy.py:376] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
INFO:tensorflow:Maybe overwriting train_steps: None
I0123 13:57:13.178883 140287364614016 config_util.py:552] Maybe overwriting train_steps: None
INFO:tensorflow:Maybe overwriting use_bfloat16: False
I0123 13:57:13.179064 140287364614016 config_util.py:552] Maybe overwriting use_bfloat16: False
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/model_lib_v2.py:564: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.
Instructions for updating:
rename to distribute_datasets_from_function
W0123 13:57:13.214003 140287364614016 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/object_detection/model_lib_v2.py:564: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.
Instructions for updating:
rename to distribute_datasets_from_function
INFO:tensorflow:Reading unweighted datasets: ['./annotations/train.record']
I0123 13:57:13.264912 140287364614016 dataset_builder.py:163] Reading unweighted datasets: ['./annotations/train.record']
INFO:tensorflow:Reading record datasets for input file: ['./annotations/train.record']
I0123 13:57:13.265283 140287364614016 dataset_builder.py:80] Reading record datasets for input file: ['./annotations/train.record']
INFO:tensorflow:Number of filenames to read: 1
I0123 13:57:13.265372 140287364614016 dataset_builder.py:81] Number of filenames to read: 1
WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.
W0123 13:57:13.265424 140287364614016 dataset_builder.py:88] num_readers has been reduced to 1 to match input file shards.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:105: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.
W0123 13:57:13.268596 140287364614016 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:105: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:237: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.map()
W0123 13:57:13.289052 140287364614016 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:237: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.map()
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.
W0123 13:57:20.676505 140287364614016 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
W0123 13:57:25.244715 140287364614016 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
2022-01-23 13:57:29.438655: W tensorflow/core/framework/op_kernel.cc:1745] OP_REQUIRES failed at multi_device_iterator_ops.cc:789 : NOT_FOUND: Resource AnonymousMultiDeviceIterator/AnonymousMultiDeviceIterator0/N10tensorflow4data12_GLOBAL__N_119MultiDeviceIteratorE does not exist.
/usr/local/lib/python3.7/dist-packages/keras/backend.py:414: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.
warnings.warn('`tf.keras.backend.set_learning_phase` is deprecated and '
INFO:tensorflow:depth of additional conv before box predictor: 0
I0123 13:57:39.527516 140281796044544 convolutional_keras_box_predictor.py:154] depth of additional conv before box predictor: 0
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
W0123 13:57:46.521125 140281796044544 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Future major versions of TensorFlow will allow gradients to flow
into the labels input on backprop by default.
See `tf.nn.softmax_cross_entropy_with_logits_v2`.
W0123 13:57:51.869955 140281796044544 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Future major versions of TensorFlow will allow gradients to flow
into the labels input on backprop by default.
See `tf.nn.softmax_cross_entropy_with_logits_v2`.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py:620: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Use fn_output_signature instead
W0123 13:58:29.798123 140281815717632 deprecation.py:551] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py:620: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Use fn_output_signature instead
INFO:tensorflow:Step 62100 per-step time 1.036s
I0123 14:00:12.977305 140287364614016 model_lib_v2.py:707] Step 62100 per-step time 1.036s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.000492997,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 90.7686,
'Loss/RPNLoss/objectness_loss': 1.3280555,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 92.09715,
'learning_rate': 0.052598022}
I0123 14:00:12.977638 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.000492997,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 90.7686,
'Loss/RPNLoss/objectness_loss': 1.3280555,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 92.09715,
'learning_rate': 0.052598022}
INFO:tensorflow:Step 62200 per-step time 0.374s
I0123 14:00:50.394164 140287364614016 model_lib_v2.py:707] Step 62200 per-step time 0.374s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0026617302,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 1.1758826,
'Loss/RPNLoss/objectness_loss': 0.15363389,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 1.3321782,
'learning_rate': 0.05235598}
I0123 14:00:50.394497 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0026617302,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 1.1758826,
'Loss/RPNLoss/objectness_loss': 0.15363389,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 1.3321782,
'learning_rate': 0.05235598}
INFO:tensorflow:Step 62300 per-step time 0.375s
I0123 14:01:27.904536 140287364614016 model_lib_v2.py:707] Step 62300 per-step time 0.375s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00060942845,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 69.3877,
'Loss/RPNLoss/objectness_loss': 1.1692628,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 70.55757,
'learning_rate': 0.05211423}
I0123 14:01:27.904881 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00060942845,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 69.3877,
'Loss/RPNLoss/objectness_loss': 1.1692628,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 70.55757,
'learning_rate': 0.05211423}
INFO:tensorflow:Step 62400 per-step time 0.378s
I0123 14:02:05.710331 140287364614016 model_lib_v2.py:707] Step 62400 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.021919131,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 91.50695,
'Loss/RPNLoss/objectness_loss': 3.7153988,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 95.24427,
'learning_rate': 0.051872768}
I0123 14:02:05.710622 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.021919131,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 91.50695,
'Loss/RPNLoss/objectness_loss': 3.7153988,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 95.24427,
'learning_rate': 0.051872768}
INFO:tensorflow:Step 62500 per-step time 0.377s
I0123 14:02:43.399406 140287364614016 model_lib_v2.py:707] Step 62500 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0004768371,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 4.623506,
'Loss/RPNLoss/objectness_loss': 3.5612764,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 8.185259,
'learning_rate': 0.051631603}
I0123 14:02:43.399716 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0004768371,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 4.623506,
'Loss/RPNLoss/objectness_loss': 3.5612764,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 8.185259,
'learning_rate': 0.051631603}
INFO:tensorflow:Step 62600 per-step time 0.371s
I0123 14:03:20.505567 140287364614016 model_lib_v2.py:707] Step 62600 per-step time 0.371s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0014855636,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 44.965633,
'Loss/RPNLoss/objectness_loss': 1.3126142,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 46.27973,
'learning_rate': 0.051390734}
I0123 14:03:20.505922 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0014855636,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 44.965633,
'Loss/RPNLoss/objectness_loss': 1.3126142,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 46.27973,
'learning_rate': 0.051390734}
INFO:tensorflow:Step 62700 per-step time 0.373s
I0123 14:03:57.809883 140287364614016 model_lib_v2.py:707] Step 62700 per-step time 0.373s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.018344786,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 6.794763,
'Loss/RPNLoss/objectness_loss': 0.51534635,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 7.328454,
'learning_rate': 0.05115015}
I0123 14:03:57.810185 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.018344786,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 6.794763,
'Loss/RPNLoss/objectness_loss': 0.51534635,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 7.328454,
'learning_rate': 0.05115015}
INFO:tensorflow:Step 62800 per-step time 0.374s
I0123 14:04:35.192644 140287364614016 model_lib_v2.py:707] Step 62800 per-step time 0.374s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00088795327,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 284.0681,
'Loss/RPNLoss/objectness_loss': 0.2403039,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 284.3093,
'learning_rate': 0.05090988}
I0123 14:04:35.192946 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00088795327,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 284.0681,
'Loss/RPNLoss/objectness_loss': 0.2403039,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 284.3093,
'learning_rate': 0.05090988}
INFO:tensorflow:Step 62900 per-step time 0.376s
I0123 14:05:12.833030 140287364614016 model_lib_v2.py:707] Step 62900 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0005971049,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 1.553404,
'Loss/RPNLoss/objectness_loss': 1.6263932,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 3.1803942,
'learning_rate': 0.05066991}
I0123 14:05:12.833358 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0005971049,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 1.553404,
'Loss/RPNLoss/objectness_loss': 1.6263932,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 3.1803942,
'learning_rate': 0.05066991}
INFO:tensorflow:Step 63000 per-step time 0.373s
I0123 14:05:50.118892 140287364614016 model_lib_v2.py:707] Step 63000 per-step time 0.373s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0021740608,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.52805334,
'Loss/RPNLoss/objectness_loss': 4.8757257,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 5.405953,
'learning_rate': 0.05043024}
I0123 14:05:50.119196 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0021740608,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.52805334,
'Loss/RPNLoss/objectness_loss': 4.8757257,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 5.405953,
'learning_rate': 0.05043024}
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 14:05:50.372031 140287364614016 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 14:05:50.373293 140287364614016 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 14:05:50.374841 140287364614016 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 14:05:50.375624 140287364614016 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 14:05:50.377091 140287364614016 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 14:05:50.377877 140287364614016 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 14:05:50.379298 140287364614016 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 14:05:50.380093 140287364614016 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 14:05:50.381747 140287364614016 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0123 14:05:50.382510 140287364614016 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Step 63100 per-step time 0.402s
I0123 14:06:30.362667 140287364614016 model_lib_v2.py:707] Step 63100 per-step time 0.402s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0018812593,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 132.10474,
'Loss/RPNLoss/objectness_loss': 0.6295264,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 132.73615,
'learning_rate': 0.050190877}
I0123 14:06:30.362978 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0018812593,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 132.10474,
'Loss/RPNLoss/objectness_loss': 0.6295264,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 132.73615,
'learning_rate': 0.050190877}
INFO:tensorflow:Step 63200 per-step time 0.373s
I0123 14:07:07.644792 140287364614016 model_lib_v2.py:707] Step 63200 per-step time 0.373s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0014057412,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 133.76497,
'Loss/RPNLoss/objectness_loss': 0.65616226,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 134.42253,
'learning_rate': 0.04995183}
I0123 14:07:07.645141 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0014057412,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 133.76497,
'Loss/RPNLoss/objectness_loss': 0.65616226,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 134.42253,
'learning_rate': 0.04995183}
INFO:tensorflow:Step 63300 per-step time 0.371s
I0123 14:07:44.710620 140287364614016 model_lib_v2.py:707] Step 63300 per-step time 0.371s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0008546056,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 274.3549,
'Loss/RPNLoss/objectness_loss': 5.545502,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 279.90125,
'learning_rate': 0.04971307}
I0123 14:07:44.710935 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0008546056,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 274.3549,
'Loss/RPNLoss/objectness_loss': 5.545502,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 279.90125,
'learning_rate': 0.04971307}
INFO:tensorflow:Step 63400 per-step time 0.375s
I0123 14:08:22.212358 140287364614016 model_lib_v2.py:707] Step 63400 per-step time 0.375s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0025572977,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 188.43549,
'Loss/RPNLoss/objectness_loss': 17.72029,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 206.15833,
'learning_rate': 0.04947465}
I0123 14:08:22.212655 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0025572977,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 188.43549,
'Loss/RPNLoss/objectness_loss': 17.72029,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 206.15833,
'learning_rate': 0.04947465}
INFO:tensorflow:Step 63500 per-step time 0.378s
I0123 14:09:00.006186 140287364614016 model_lib_v2.py:707] Step 63500 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0005873506,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 155.0148,
'Loss/RPNLoss/objectness_loss': 1.0824015,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 156.0978,
'learning_rate': 0.04923654}
I0123 14:09:00.006499 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0005873506,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 155.0148,
'Loss/RPNLoss/objectness_loss': 1.0824015,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 156.0978,
'learning_rate': 0.04923654}
INFO:tensorflow:Step 63600 per-step time 0.375s
I0123 14:09:37.546933 140287364614016 model_lib_v2.py:707] Step 63600 per-step time 0.375s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0011416264,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 338.46606,
'Loss/RPNLoss/objectness_loss': 2.061144,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 340.52835,
'learning_rate': 0.048998754}
I0123 14:09:37.547255 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0011416264,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 338.46606,
'Loss/RPNLoss/objectness_loss': 2.061144,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 340.52835,
'learning_rate': 0.048998754}
INFO:tensorflow:Step 63700 per-step time 0.371s
I0123 14:10:14.661952 140287364614016 model_lib_v2.py:707] Step 63700 per-step time 0.371s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0016434728,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 29.517916,
'Loss/RPNLoss/objectness_loss': 8.221753,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 37.74131,
'learning_rate': 0.048761286}
I0123 14:10:14.662243 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0016434728,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 29.517916,
'Loss/RPNLoss/objectness_loss': 8.221753,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 37.74131,
'learning_rate': 0.048761286}
INFO:tensorflow:Step 63800 per-step time 0.372s
I0123 14:10:51.910885 140287364614016 model_lib_v2.py:707] Step 63800 per-step time 0.372s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0007961419,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 35.16668,
'Loss/RPNLoss/objectness_loss': 4.527713,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 39.69519,
'learning_rate': 0.04852413}
I0123 14:10:51.911220 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0007961419,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 35.16668,
'Loss/RPNLoss/objectness_loss': 4.527713,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 39.69519,
'learning_rate': 0.04852413}
INFO:tensorflow:Step 63900 per-step time 0.370s
I0123 14:11:28.960614 140287364614016 model_lib_v2.py:707] Step 63900 per-step time 0.370s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00039884815,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 4.4438143,
'Loss/RPNLoss/objectness_loss': 8.473839,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 12.918052,
'learning_rate': 0.048287313}
I0123 14:11:28.960958 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00039884815,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 4.4438143,
'Loss/RPNLoss/objectness_loss': 8.473839,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 12.918052,
'learning_rate': 0.048287313}
INFO:tensorflow:Step 64000 per-step time 0.373s
I0123 14:12:06.213313 140287364614016 model_lib_v2.py:707] Step 64000 per-step time 0.373s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00033277879,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 6.043744,
'Loss/RPNLoss/objectness_loss': 7.984674,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 14.028751,
'learning_rate': 0.04805083}
I0123 14:12:06.213598 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00033277879,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 6.043744,
'Loss/RPNLoss/objectness_loss': 7.984674,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 14.028751,
'learning_rate': 0.04805083}
INFO:tensorflow:Step 64100 per-step time 0.413s
I0123 14:12:47.546821 140287364614016 model_lib_v2.py:707] Step 64100 per-step time 0.413s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.7242764,
'Loss/BoxClassifierLoss/localization_loss': 0.61703694,
'Loss/RPNLoss/localization_loss': 329.75446,
'Loss/RPNLoss/objectness_loss': 1.200119,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 332.2959,
'learning_rate': 0.047814675}
I0123 14:12:47.547163 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.7242764,
'Loss/BoxClassifierLoss/localization_loss': 0.61703694,
'Loss/RPNLoss/localization_loss': 329.75446,
'Loss/RPNLoss/objectness_loss': 1.200119,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 332.2959,
'learning_rate': 0.047814675}
INFO:tensorflow:Step 64200 per-step time 0.374s
I0123 14:13:24.963876 140287364614016 model_lib_v2.py:707] Step 64200 per-step time 0.374s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00055178674,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 319.90347,
'Loss/RPNLoss/objectness_loss': 2.7614956,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 322.66553,
'learning_rate': 0.047578853}
I0123 14:13:24.964197 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00055178674,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 319.90347,
'Loss/RPNLoss/objectness_loss': 2.7614956,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 322.66553,
'learning_rate': 0.047578853}
INFO:tensorflow:Step 64300 per-step time 0.376s
I0123 14:14:02.543442 140287364614016 model_lib_v2.py:707] Step 64300 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.009720907,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 97.20221,
'Loss/RPNLoss/objectness_loss': 1.3665836,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 98.578514,
'learning_rate': 0.04734336}
I0123 14:14:02.543759 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.009720907,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 97.20221,
'Loss/RPNLoss/objectness_loss': 1.3665836,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 98.578514,
'learning_rate': 0.04734336}
INFO:tensorflow:Step 64400 per-step time 0.376s
I0123 14:14:40.139990 140287364614016 model_lib_v2.py:707] Step 64400 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0004939477,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 53.6672,
'Loss/RPNLoss/objectness_loss': 6.200095,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 59.867786,
'learning_rate': 0.04710822}
I0123 14:14:40.140307 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0004939477,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 53.6672,
'Loss/RPNLoss/objectness_loss': 6.200095,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 59.867786,
'learning_rate': 0.04710822}
INFO:tensorflow:Step 64500 per-step time 0.374s
I0123 14:15:17.541444 140287364614016 model_lib_v2.py:707] Step 64500 per-step time 0.374s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.016984738,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.09159162,
'Loss/RPNLoss/objectness_loss': 2.2853708,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 2.3939471,
'learning_rate': 0.046873417}
I0123 14:15:17.541775 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.016984738,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.09159162,
'Loss/RPNLoss/objectness_loss': 2.2853708,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 2.3939471,
'learning_rate': 0.046873417}
INFO:tensorflow:Step 64600 per-step time 0.374s
I0123 14:15:54.930982 140287364614016 model_lib_v2.py:707] Step 64600 per-step time 0.374s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.004966923,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 197.84238,
'Loss/RPNLoss/objectness_loss': 2.8513966,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 200.69875,
'learning_rate': 0.046638947}
I0123 14:15:54.931288 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.004966923,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 197.84238,
'Loss/RPNLoss/objectness_loss': 2.8513966,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 200.69875,
'learning_rate': 0.046638947}
INFO:tensorflow:Step 64700 per-step time 0.373s
I0123 14:16:32.224833 140287364614016 model_lib_v2.py:707] Step 64700 per-step time 0.373s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.018832427,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 49.595833,
'Loss/RPNLoss/objectness_loss': 2.1529121,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 51.76758,
'learning_rate': 0.046404842}
I0123 14:16:32.225135 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.018832427,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 49.595833,
'Loss/RPNLoss/objectness_loss': 2.1529121,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 51.76758,
'learning_rate': 0.046404842}
INFO:tensorflow:Step 64800 per-step time 0.381s
I0123 14:17:10.304227 140287364614016 model_lib_v2.py:707] Step 64800 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.087695256,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 350.02173,
'Loss/RPNLoss/objectness_loss': 1.1198058,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 351.22922,
'learning_rate': 0.046171073}
I0123 14:17:10.304567 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.087695256,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 350.02173,
'Loss/RPNLoss/objectness_loss': 1.1198058,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 351.22922,
'learning_rate': 0.046171073}
INFO:tensorflow:Step 64900 per-step time 0.374s
I0123 14:17:47.677740 140287364614016 model_lib_v2.py:707] Step 64900 per-step time 0.374s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0014196797,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 534.66064,
'Loss/RPNLoss/objectness_loss': 6.2795687,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 540.94165,
'learning_rate': 0.04593765}
I0123 14:17:47.678031 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0014196797,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 534.66064,
'Loss/RPNLoss/objectness_loss': 6.2795687,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 540.94165,
'learning_rate': 0.04593765}
INFO:tensorflow:Step 65000 per-step time 0.372s
I0123 14:18:24.830154 140287364614016 model_lib_v2.py:707] Step 65000 per-step time 0.372s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.015616467,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 68.459785,
'Loss/RPNLoss/objectness_loss': 4.759358,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 73.23476,
'learning_rate': 0.045704603}
I0123 14:18:24.830453 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.015616467,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 68.459785,
'Loss/RPNLoss/objectness_loss': 4.759358,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 73.23476,
'learning_rate': 0.045704603}
INFO:tensorflow:Step 65100 per-step time 0.407s
I0123 14:19:05.559003 140287364614016 model_lib_v2.py:707] Step 65100 per-step time 0.407s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.031776946,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 160.14133,
'Loss/RPNLoss/objectness_loss': 0.8498359,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 161.02293,
'learning_rate': 0.045471888}
I0123 14:19:05.559355 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.031776946,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 160.14133,
'Loss/RPNLoss/objectness_loss': 0.8498359,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 161.02293,
'learning_rate': 0.045471888}
INFO:tensorflow:Step 65200 per-step time 0.377s
I0123 14:19:43.209518 140287364614016 model_lib_v2.py:707] Step 65200 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.016326264,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 52.032177,
'Loss/RPNLoss/objectness_loss': 0.745766,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 52.79427,
'learning_rate': 0.045239534}
I0123 14:19:43.209821 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.016326264,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 52.032177,
'Loss/RPNLoss/objectness_loss': 0.745766,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 52.79427,
'learning_rate': 0.045239534}
INFO:tensorflow:Step 65300 per-step time 0.374s
I0123 14:20:20.585823 140287364614016 model_lib_v2.py:707] Step 65300 per-step time 0.374s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0007256042,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 55.534836,
'Loss/RPNLoss/objectness_loss': 0.20595771,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 55.74152,
'learning_rate': 0.04500754}
I0123 14:20:20.586178 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0007256042,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 55.534836,
'Loss/RPNLoss/objectness_loss': 0.20595771,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 55.74152,
'learning_rate': 0.04500754}
INFO:tensorflow:Step 65400 per-step time 0.377s
I0123 14:20:58.322028 140287364614016 model_lib_v2.py:707] Step 65400 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.043169294,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 1.1413682,
'Loss/RPNLoss/objectness_loss': 7.871082,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 9.055619,
'learning_rate': 0.044775933}
I0123 14:20:58.322373 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.043169294,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 1.1413682,
'Loss/RPNLoss/objectness_loss': 7.871082,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 9.055619,
'learning_rate': 0.044775933}
INFO:tensorflow:Step 65500 per-step time 0.376s
I0123 14:21:35.873519 140287364614016 model_lib_v2.py:707] Step 65500 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0011358516,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 197.06683,
'Loss/RPNLoss/objectness_loss': 12.7355385,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 209.80351,
'learning_rate': 0.044544667}
I0123 14:21:35.873832 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0011358516,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 197.06683,
'Loss/RPNLoss/objectness_loss': 12.7355385,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 209.80351,
'learning_rate': 0.044544667}
INFO:tensorflow:Step 65600 per-step time 0.370s
I0123 14:22:12.915478 140287364614016 model_lib_v2.py:707] Step 65600 per-step time 0.370s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00078448025,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 309.0344,
'Loss/RPNLoss/objectness_loss': 1.9329586,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 310.96814,
'learning_rate': 0.044313792}
I0123 14:22:12.915791 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00078448025,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 309.0344,
'Loss/RPNLoss/objectness_loss': 1.9329586,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 310.96814,
'learning_rate': 0.044313792}
INFO:tensorflow:Step 65700 per-step time 0.378s
I0123 14:22:50.677616 140287364614016 model_lib_v2.py:707] Step 65700 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 2.0768228,
'Loss/BoxClassifierLoss/localization_loss': 1.6045876,
'Loss/RPNLoss/localization_loss': 2.2951121,
'Loss/RPNLoss/objectness_loss': 0.1115912,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 6.088114,
'learning_rate': 0.044083264}
I0123 14:22:50.677938 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 2.0768228,
'Loss/BoxClassifierLoss/localization_loss': 1.6045876,
'Loss/RPNLoss/localization_loss': 2.2951121,
'Loss/RPNLoss/objectness_loss': 0.1115912,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 6.088114,
'learning_rate': 0.044083264}
INFO:tensorflow:Step 65800 per-step time 0.376s
I0123 14:23:28.280859 140287364614016 model_lib_v2.py:707] Step 65800 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00073656684,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 66.14475,
'Loss/RPNLoss/objectness_loss': 1.62201,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 67.7675,
'learning_rate': 0.043853115}
I0123 14:23:28.281158 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00073656684,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 66.14475,
'Loss/RPNLoss/objectness_loss': 1.62201,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 67.7675,
'learning_rate': 0.043853115}
INFO:tensorflow:Step 65900 per-step time 0.375s
I0123 14:24:05.789642 140287364614016 model_lib_v2.py:707] Step 65900 per-step time 0.375s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0053697806,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 69.401436,
'Loss/RPNLoss/objectness_loss': 2.7582493,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 72.165054,
'learning_rate': 0.043623343}
I0123 14:24:05.789956 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0053697806,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 69.401436,
'Loss/RPNLoss/objectness_loss': 2.7582493,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 72.165054,
'learning_rate': 0.043623343}
INFO:tensorflow:Step 66000 per-step time 0.379s
I0123 14:24:43.694464 140287364614016 model_lib_v2.py:707] Step 66000 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.000523897,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 59.15332,
'Loss/RPNLoss/objectness_loss': 4.1302223,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 63.284065,
'learning_rate': 0.043393955}
I0123 14:24:43.694800 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.000523897,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 59.15332,
'Loss/RPNLoss/objectness_loss': 4.1302223,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 63.284065,
'learning_rate': 0.043393955}
INFO:tensorflow:Step 66100 per-step time 0.398s
I0123 14:25:23.534175 140287364614016 model_lib_v2.py:707] Step 66100 per-step time 0.398s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0005418784,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 1.4355414,
'Loss/RPNLoss/objectness_loss': 2.4417148,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 3.877798,
'learning_rate': 0.04316494}
I0123 14:25:23.534489 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0005418784,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 1.4355414,
'Loss/RPNLoss/objectness_loss': 2.4417148,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 3.877798,
'learning_rate': 0.04316494}
INFO:tensorflow:Step 66200 per-step time 0.375s
I0123 14:26:01.000761 140287364614016 model_lib_v2.py:707] Step 66200 per-step time 0.375s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0005302159,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 234.18225,
'Loss/RPNLoss/objectness_loss': 4.2425914,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 238.42537,
'learning_rate': 0.042936318}
I0123 14:26:01.001060 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0005302159,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 234.18225,
'Loss/RPNLoss/objectness_loss': 4.2425914,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 238.42537,
'learning_rate': 0.042936318}
INFO:tensorflow:Step 66300 per-step time 0.377s
I0123 14:26:38.728879 140287364614016 model_lib_v2.py:707] Step 66300 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0007565207,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 1.5628887,
'Loss/RPNLoss/objectness_loss': 2.5444858,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 4.108131,
'learning_rate': 0.042708073}
I0123 14:26:38.729179 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0007565207,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 1.5628887,
'Loss/RPNLoss/objectness_loss': 2.5444858,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.042708073}
INFO:tensorflow:Step 66400 per-step time 0.378s
I0123 14:27:16.495694 140287364614016 model_lib_v2.py:707] Step 66400 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.001633033,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.9909986,
'Loss/RPNLoss/objectness_loss': 0.34291345,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 1.3355451,
'learning_rate': 0.042480204}
I0123 14:27:16.496026 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.001633033,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.9909986,
'Loss/RPNLoss/objectness_loss': 0.34291345,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.042480204}
INFO:tensorflow:Step 66500 per-step time 0.373s
I0123 14:27:53.822713 140287364614016 model_lib_v2.py:707] Step 66500 per-step time 0.373s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0009110337,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 18.840927,
'Loss/RPNLoss/objectness_loss': 1.5229821,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 20.36482,
'learning_rate': 0.04225275}
I0123 14:27:53.823029 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0009110337,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 18.840927,
'Loss/RPNLoss/objectness_loss': 1.5229821,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.04225275}
INFO:tensorflow:Step 66600 per-step time 0.376s
I0123 14:28:31.392341 140287364614016 model_lib_v2.py:707] Step 66600 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.008831313,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 186.84494,
'Loss/RPNLoss/objectness_loss': 1.1450272,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 187.9988,
'learning_rate': 0.042025667}
I0123 14:28:31.392649 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.008831313,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 186.84494,
'Loss/RPNLoss/objectness_loss': 1.1450272,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.042025667}
INFO:tensorflow:Step 66700 per-step time 0.378s
I0123 14:29:09.201788 140287364614016 model_lib_v2.py:707] Step 66700 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.90662026,
'Loss/BoxClassifierLoss/localization_loss': 1.3705763,
'Loss/RPNLoss/localization_loss': 129.9196,
'Loss/RPNLoss/objectness_loss': 0.3341232,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 132.53091,
'learning_rate': 0.041798975}
I0123 14:29:09.202142 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.90662026,
'Loss/BoxClassifierLoss/localization_loss': 1.3705763,
'Loss/RPNLoss/localization_loss': 129.9196,
'Loss/RPNLoss/objectness_loss': 0.3341232,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.041798975}
INFO:tensorflow:Step 66800 per-step time 0.378s
I0123 14:29:46.990561 140287364614016 model_lib_v2.py:707] Step 66800 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.5758336,
'Loss/BoxClassifierLoss/localization_loss': 0.37216204,
'Loss/RPNLoss/localization_loss': 463.5587,
'Loss/RPNLoss/objectness_loss': 1.7162435,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 466.22293,
'learning_rate': 0.041572697}
I0123 14:29:46.990869 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.5758336,
'Loss/BoxClassifierLoss/localization_loss': 0.37216204,
'Loss/RPNLoss/localization_loss': 463.5587,
'Loss/RPNLoss/objectness_loss': 1.7162435,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.041572697}
INFO:tensorflow:Step 66900 per-step time 0.376s
I0123 14:30:24.563529 140287364614016 model_lib_v2.py:707] Step 66900 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.001158542,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 151.71655,
'Loss/RPNLoss/objectness_loss': 1.5667381,
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'Loss/total_loss': 153.28445,
'learning_rate': 0.041346807}
I0123 14:30:24.563854 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.001158542,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 1.5667381,
'Loss/regularization_loss': 0.0,
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INFO:tensorflow:Step 67000 per-step time 0.376s
I0123 14:31:02.127937 140287364614016 model_lib_v2.py:707] Step 67000 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0007057325,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.30018267,
'Loss/RPNLoss/objectness_loss': 1.8499525,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 2.1508408,
'learning_rate': 0.041121315}
I0123 14:31:02.128259 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0007057325,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 1.8499525,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.041121315}
INFO:tensorflow:Step 67100 per-step time 0.416s
I0123 14:31:43.727134 140287364614016 model_lib_v2.py:707] Step 67100 per-step time 0.416s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.101306155,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.619981,
'Loss/RPNLoss/objectness_loss': 2.606194,
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'Loss/total_loss': 3.3274813,
'learning_rate': 0.040896244}
I0123 14:31:43.727470 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.101306155,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 2.606194,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 3.3274813,
'learning_rate': 0.040896244}
INFO:tensorflow:Step 67200 per-step time 0.379s
I0123 14:32:21.583734 140287364614016 model_lib_v2.py:707] Step 67200 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.15257522,
'Loss/BoxClassifierLoss/localization_loss': 0.14359897,
'Loss/RPNLoss/localization_loss': 2.3606224,
'Loss/RPNLoss/objectness_loss': 0.055753827,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 2.7125504,
'learning_rate': 0.04067156}
I0123 14:32:21.584040 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.15257522,
'Loss/BoxClassifierLoss/localization_loss': 0.14359897,
'Loss/RPNLoss/localization_loss': 2.3606224,
'Loss/RPNLoss/objectness_loss': 0.055753827,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 2.7125504,
'learning_rate': 0.04067156}
INFO:tensorflow:Step 67300 per-step time 0.373s
I0123 14:32:58.889015 140287364614016 model_lib_v2.py:707] Step 67300 per-step time 0.373s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.010589155,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 9.956725,
'Loss/RPNLoss/objectness_loss': 1.6576607,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 11.624975,
'learning_rate': 0.040447306}
I0123 14:32:58.889334 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.010589155,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 9.956725,
'Loss/RPNLoss/objectness_loss': 1.6576607,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 11.624975,
'learning_rate': 0.040447306}
INFO:tensorflow:Step 67400 per-step time 0.377s
I0123 14:33:36.629634 140287364614016 model_lib_v2.py:707] Step 67400 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00045717304,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 190.40985,
'Loss/RPNLoss/objectness_loss': 4.6447096,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 195.05502,
'learning_rate': 0.040223427}
I0123 14:33:36.629938 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00045717304,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 190.40985,
'Loss/RPNLoss/objectness_loss': 4.6447096,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 195.05502,
'learning_rate': 0.040223427}
INFO:tensorflow:Step 67500 per-step time 0.376s
I0123 14:34:14.225439 140287364614016 model_lib_v2.py:707] Step 67500 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0005166128,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 4.8568997,
'Loss/RPNLoss/objectness_loss': 0.6456769,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 5.5030932,
'learning_rate': 0.039999995}
I0123 14:34:14.225737 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0005166128,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 4.8568997,
'Loss/RPNLoss/objectness_loss': 0.6456769,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 5.5030932,
'learning_rate': 0.039999995}
INFO:tensorflow:Step 67600 per-step time 0.381s
I0123 14:34:52.289035 140287364614016 model_lib_v2.py:707] Step 67600 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.016063936,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 89.55681,
'Loss/RPNLoss/objectness_loss': 0.28165355,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 89.85452,
'learning_rate': 0.03977696}
I0123 14:34:52.289359 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.016063936,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 89.55681,
'Loss/RPNLoss/objectness_loss': 0.28165355,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 89.85452,
'learning_rate': 0.03977696}
INFO:tensorflow:Step 67700 per-step time 0.375s
I0123 14:35:29.773698 140287364614016 model_lib_v2.py:707] Step 67700 per-step time 0.375s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.011335067,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.3398994,
'Loss/RPNLoss/objectness_loss': 0.19628412,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 0.5475186,
'learning_rate': 0.03955436}
I0123 14:35:29.774045 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.011335067,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.3398994,
'Loss/RPNLoss/objectness_loss': 0.19628412,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 0.5475186,
'learning_rate': 0.03955436}
INFO:tensorflow:Step 67800 per-step time 0.380s
I0123 14:36:07.801058 140287364614016 model_lib_v2.py:707] Step 67800 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.5717468,
'Loss/BoxClassifierLoss/localization_loss': 0.31318742,
'Loss/RPNLoss/localization_loss': 0.15776302,
'Loss/RPNLoss/objectness_loss': 2.709584,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 3.7522812,
'learning_rate': 0.03933216}
I0123 14:36:07.801362 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.5717468,
'Loss/BoxClassifierLoss/localization_loss': 0.31318742,
'Loss/RPNLoss/localization_loss': 0.15776302,
'Loss/RPNLoss/objectness_loss': 2.709584,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 3.7522812,
'learning_rate': 0.03933216}
INFO:tensorflow:Step 67900 per-step time 0.378s
I0123 14:36:45.615117 140287364614016 model_lib_v2.py:707] Step 67900 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.027279086,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.07625271,
'Loss/RPNLoss/objectness_loss': 5.6926556,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 5.7961874,
'learning_rate': 0.03911039}
I0123 14:36:45.615448 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.027279086,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.07625271,
'Loss/RPNLoss/objectness_loss': 5.6926556,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 5.7961874,
'learning_rate': 0.03911039}
INFO:tensorflow:Step 68000 per-step time 0.375s
I0123 14:37:23.139506 140287364614016 model_lib_v2.py:707] Step 68000 per-step time 0.375s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.04281176,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 81.70731,
'Loss/RPNLoss/objectness_loss': 1.007916,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 82.75804,
'learning_rate': 0.038889054}
I0123 14:37:23.139845 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.04281176,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 81.70731,
'Loss/RPNLoss/objectness_loss': 1.007916,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 82.75804,
'learning_rate': 0.038889054}
INFO:tensorflow:Step 68100 per-step time 0.414s
I0123 14:38:04.493536 140287364614016 model_lib_v2.py:707] Step 68100 per-step time 0.414s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00050452765,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 104.790115,
'Loss/RPNLoss/objectness_loss': 0.8132393,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 105.60386,
'learning_rate': 0.03866813}
I0123 14:38:04.493857 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00050452765,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 104.790115,
'Loss/RPNLoss/objectness_loss': 0.8132393,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 105.60386,
'learning_rate': 0.03866813}
INFO:tensorflow:Step 68200 per-step time 0.376s
I0123 14:38:42.066681 140287364614016 model_lib_v2.py:707] Step 68200 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0013446165,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 38.99012,
'Loss/RPNLoss/objectness_loss': 1.2091303,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 40.200596,
'learning_rate': 0.038447637}
I0123 14:38:42.067008 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0013446165,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 38.99012,
'Loss/RPNLoss/objectness_loss': 1.2091303,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 40.200596,
'learning_rate': 0.038447637}
INFO:tensorflow:Step 68300 per-step time 0.378s
I0123 14:39:19.901208 140287364614016 model_lib_v2.py:707] Step 68300 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.002341417,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 190.38118,
'Loss/RPNLoss/objectness_loss': 2.5381734,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 192.92169,
'learning_rate': 0.03822759}
I0123 14:39:19.901573 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.002341417,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 190.38118,
'Loss/RPNLoss/objectness_loss': 2.5381734,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 192.92169,
'learning_rate': 0.03822759}
INFO:tensorflow:Step 68400 per-step time 0.379s
I0123 14:39:57.781658 140287364614016 model_lib_v2.py:707] Step 68400 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0013594169,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 4.256328,
'Loss/RPNLoss/objectness_loss': 3.376622,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 7.63431,
'learning_rate': 0.03800796}
I0123 14:39:57.781968 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0013594169,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 4.256328,
'Loss/RPNLoss/objectness_loss': 3.376622,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 7.63431,
'learning_rate': 0.03800796}
INFO:tensorflow:Step 68500 per-step time 0.379s
I0123 14:40:35.701738 140287364614016 model_lib_v2.py:707] Step 68500 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0023285174,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 3.2088563,
'Loss/RPNLoss/objectness_loss': 5.407342,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 8.618526,
'learning_rate': 0.037788767}
I0123 14:40:35.702041 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0023285174,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 3.2088563,
'Loss/RPNLoss/objectness_loss': 5.407342,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 8.618526,
'learning_rate': 0.037788767}
INFO:tensorflow:Step 68600 per-step time 0.378s
I0123 14:41:13.507974 140287364614016 model_lib_v2.py:707] Step 68600 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00074478844,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.6535865,
'Loss/RPNLoss/objectness_loss': 0.31132272,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 0.965654,
'learning_rate': 0.037570026}
I0123 14:41:13.508322 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00074478844,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.6535865,
'Loss/RPNLoss/objectness_loss': 0.31132272,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 0.965654,
'learning_rate': 0.037570026}
INFO:tensorflow:Step 68700 per-step time 0.378s
I0123 14:41:51.343844 140287364614016 model_lib_v2.py:707] Step 68700 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00077436573,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 67.464645,
'Loss/RPNLoss/objectness_loss': 0.87809336,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 68.34351,
'learning_rate': 0.037351716}
I0123 14:41:51.344183 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00077436573,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 67.464645,
'Loss/RPNLoss/objectness_loss': 0.87809336,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 68.34351,
'learning_rate': 0.037351716}
INFO:tensorflow:Step 68800 per-step time 0.377s
I0123 14:42:29.061659 140287364614016 model_lib_v2.py:707] Step 68800 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0024344341,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 11.448629,
'Loss/RPNLoss/objectness_loss': 0.62004596,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 12.07111,
'learning_rate': 0.03713386}
I0123 14:42:29.061973 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0024344341,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 11.448629,
'Loss/RPNLoss/objectness_loss': 0.62004596,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 12.07111,
'learning_rate': 0.03713386}
INFO:tensorflow:Step 68900 per-step time 0.380s
I0123 14:43:07.058868 140287364614016 model_lib_v2.py:707] Step 68900 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.02223115,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 69.90169,
'Loss/RPNLoss/objectness_loss': 0.658229,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 70.582146,
'learning_rate': 0.036916416}
I0123 14:43:07.059190 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.02223115,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 69.90169,
'Loss/RPNLoss/objectness_loss': 0.658229,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 70.582146,
'learning_rate': 0.036916416}
INFO:tensorflow:Step 69000 per-step time 0.377s
I0123 14:43:44.740846 140287364614016 model_lib_v2.py:707] Step 69000 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0005415403,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 104.8678,
'Loss/RPNLoss/objectness_loss': 3.850308,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 108.71865,
'learning_rate': 0.036699455}
I0123 14:43:44.741152 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0005415403,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 104.8678,
'Loss/RPNLoss/objectness_loss': 3.850308,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 108.71865,
'learning_rate': 0.036699455}
INFO:tensorflow:Step 69100 per-step time 0.409s
I0123 14:44:25.689075 140287364614016 model_lib_v2.py:707] Step 69100 per-step time 0.409s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.646467,
'Loss/BoxClassifierLoss/localization_loss': 0.27335536,
'Loss/RPNLoss/localization_loss': 213.20052,
'Loss/RPNLoss/objectness_loss': 0.070837475,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 214.19118,
'learning_rate': 0.036482926}
I0123 14:44:25.689393 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.646467,
'Loss/BoxClassifierLoss/localization_loss': 0.27335536,
'Loss/RPNLoss/localization_loss': 213.20052,
'Loss/RPNLoss/objectness_loss': 0.070837475,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 214.19118,
'learning_rate': 0.036482926}
INFO:tensorflow:Step 69200 per-step time 0.384s
I0123 14:45:04.053019 140287364614016 model_lib_v2.py:707] Step 69200 per-step time 0.384s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0007440754,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 92.8174,
'Loss/RPNLoss/objectness_loss': 2.9257832,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 95.74393,
'learning_rate': 0.036266863}
I0123 14:45:04.053343 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0007440754,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 92.8174,
'Loss/RPNLoss/objectness_loss': 2.9257832,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 95.74393,
'learning_rate': 0.036266863}
INFO:tensorflow:Step 69300 per-step time 0.379s
I0123 14:45:41.936893 140287364614016 model_lib_v2.py:707] Step 69300 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0037911234,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 108.92578,
'Loss/RPNLoss/objectness_loss': 1.7931037,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 110.72268,
'learning_rate': 0.03605124}
I0123 14:45:41.937188 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0037911234,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 108.92578,
'Loss/RPNLoss/objectness_loss': 1.7931037,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 110.72268,
'learning_rate': 0.03605124}
INFO:tensorflow:Step 69400 per-step time 0.376s
I0123 14:46:19.561093 140287364614016 model_lib_v2.py:707] Step 69400 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.018804133,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 4.710282,
'Loss/RPNLoss/objectness_loss': 8.5800085,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 13.309095,
'learning_rate': 0.03583609}
I0123 14:46:19.561381 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.018804133,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 4.710282,
'Loss/RPNLoss/objectness_loss': 8.5800085,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 13.309095,
'learning_rate': 0.03583609}
INFO:tensorflow:Step 69500 per-step time 0.381s
I0123 14:46:57.667685 140287364614016 model_lib_v2.py:707] Step 69500 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00054142124,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 1.6460152,
'Loss/RPNLoss/objectness_loss': 6.075554,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 7.7221103,
'learning_rate': 0.03562138}
I0123 14:46:57.668014 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00054142124,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 1.6460152,
'Loss/RPNLoss/objectness_loss': 6.075554,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 7.7221103,
'learning_rate': 0.03562138}
INFO:tensorflow:Step 69600 per-step time 0.377s
I0123 14:47:35.402656 140287364614016 model_lib_v2.py:707] Step 69600 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00054321403,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 135.05266,
'Loss/RPNLoss/objectness_loss': 0.571265,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 135.62447,
'learning_rate': 0.035407133}
I0123 14:47:35.402967 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00054321403,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 135.05266,
'Loss/RPNLoss/objectness_loss': 0.571265,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 135.62447,
'learning_rate': 0.035407133}
INFO:tensorflow:Step 69700 per-step time 0.377s
I0123 14:48:13.134203 140287364614016 model_lib_v2.py:707] Step 69700 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0006714335,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 5.8901715,
'Loss/RPNLoss/objectness_loss': 2.0861309,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 7.9769735,
'learning_rate': 0.035193346}
I0123 14:48:13.134526 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0006714335,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 5.8901715,
'Loss/RPNLoss/objectness_loss': 2.0861309,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 7.9769735,
'learning_rate': 0.035193346}
INFO:tensorflow:Step 69800 per-step time 0.378s
I0123 14:48:50.894316 140287364614016 model_lib_v2.py:707] Step 69800 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.012285953,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.064982906,
'Loss/RPNLoss/objectness_loss': 1.0114379,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 1.0887067,
'learning_rate': 0.034980044}
I0123 14:48:50.894630 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.012285953,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.064982906,
'Loss/RPNLoss/objectness_loss': 1.0114379,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 1.0887067,
'learning_rate': 0.034980044}
INFO:tensorflow:Step 69900 per-step time 0.377s
I0123 14:49:28.627942 140287364614016 model_lib_v2.py:707] Step 69900 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.04658323,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 96.22293,
'Loss/RPNLoss/objectness_loss': 0.13149741,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 96.40101,
'learning_rate': 0.034767188}
I0123 14:49:28.628266 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.04658323,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 96.22293,
'Loss/RPNLoss/objectness_loss': 0.13149741,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 96.40101,
'learning_rate': 0.034767188}
INFO:tensorflow:Step 70000 per-step time 0.378s
I0123 14:50:06.423958 140287364614016 model_lib_v2.py:707] Step 70000 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0005913003,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 4.9126196,
'Loss/RPNLoss/objectness_loss': 1.958952,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 6.872163,
'learning_rate': 0.034554806}
I0123 14:50:06.424284 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0005913003,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 4.9126196,
'Loss/RPNLoss/objectness_loss': 1.958952,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 6.872163,
'learning_rate': 0.034554806}
INFO:tensorflow:Step 70100 per-step time 0.411s
I0123 14:50:47.543917 140287364614016 model_lib_v2.py:707] Step 70100 per-step time 0.411s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00530028,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 154.0235,
'Loss/RPNLoss/objectness_loss': 1.7925503,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 155.82135,
'learning_rate': 0.03434291}
I0123 14:50:47.544255 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00530028,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 1.7925503,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 155.82135,
'learning_rate': 0.03434291}
INFO:tensorflow:Step 70200 per-step time 0.373s
I0123 14:51:24.872925 140287364614016 model_lib_v2.py:707] Step 70200 per-step time 0.373s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00088127406,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.19978368,
'Loss/RPNLoss/objectness_loss': 1.6303716,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 1.8310366,
'learning_rate': 0.034131475}
I0123 14:51:24.873330 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00088127406,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 1.6303716,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.034131475}
INFO:tensorflow:Step 70300 per-step time 0.374s
I0123 14:52:02.264473 140287364614016 model_lib_v2.py:707] Step 70300 per-step time 0.374s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0013589845,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 77.718056,
'Loss/RPNLoss/objectness_loss': 0.53533536,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 78.25475,
'learning_rate': 0.03392053}
I0123 14:52:02.264772 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0013589845,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 0.53533536,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.03392053}
INFO:tensorflow:Step 70400 per-step time 0.372s
I0123 14:52:39.455614 140287364614016 model_lib_v2.py:707] Step 70400 per-step time 0.372s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00062303204,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 72.64623,
'Loss/RPNLoss/objectness_loss': 0.23421729,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 72.88107,
'learning_rate': 0.033710044}
I0123 14:52:39.455928 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00062303204,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 72.64623,
'Loss/RPNLoss/objectness_loss': 0.23421729,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.033710044}
INFO:tensorflow:Step 70500 per-step time 0.377s
I0123 14:53:17.178638 140287364614016 model_lib_v2.py:707] Step 70500 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0057256985,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 167.31453,
'Loss/RPNLoss/objectness_loss': 4.22501,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 171.54526,
'learning_rate': 0.033500046}
I0123 14:53:17.178962 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0057256985,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 167.31453,
'Loss/RPNLoss/objectness_loss': 4.22501,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 171.54526,
'learning_rate': 0.033500046}
INFO:tensorflow:Step 70600 per-step time 0.373s
I0123 14:53:54.508365 140287364614016 model_lib_v2.py:707] Step 70600 per-step time 0.373s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0005274398,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 110.11534,
'Loss/RPNLoss/objectness_loss': 4.392782,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 114.50865,
'learning_rate': 0.033290528}
I0123 14:53:54.508695 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0005274398,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 110.11534,
'Loss/RPNLoss/objectness_loss': 4.392782,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.033290528}
INFO:tensorflow:Step 70700 per-step time 0.373s
I0123 14:54:31.834865 140287364614016 model_lib_v2.py:707] Step 70700 per-step time 0.373s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0006035818,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 7.2183175,
'Loss/RPNLoss/objectness_loss': 7.616486,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 14.835407,
'learning_rate': 0.033081505}
I0123 14:54:31.835172 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0006035818,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 7.2183175,
'Loss/RPNLoss/objectness_loss': 7.616486,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 14.835407,
'learning_rate': 0.033081505}
INFO:tensorflow:Step 70800 per-step time 0.379s
I0123 14:55:09.689151 140287364614016 model_lib_v2.py:707] Step 70800 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.021801695,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 32.38252,
'Loss/RPNLoss/objectness_loss': 0.65434825,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 33.05867,
'learning_rate': 0.032872964}
I0123 14:55:09.689477 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.021801695,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 32.38252,
'Loss/RPNLoss/objectness_loss': 0.65434825,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 33.05867,
'learning_rate': 0.032872964}
INFO:tensorflow:Step 70900 per-step time 0.375s
I0123 14:55:47.178351 140287364614016 model_lib_v2.py:707] Step 70900 per-step time 0.375s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00084949925,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 1.1335633,
'Loss/RPNLoss/objectness_loss': 0.8482764,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 1.9826891,
'learning_rate': 0.032664917}
I0123 14:55:47.178681 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00084949925,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 0.8482764,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 1.9826891,
'learning_rate': 0.032664917}
INFO:tensorflow:Step 71000 per-step time 0.377s
I0123 14:56:24.918713 140287364614016 model_lib_v2.py:707] Step 71000 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.053898003,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 72.32569,
'Loss/RPNLoss/objectness_loss': 0.62803006,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 73.00762,
'learning_rate': 0.03245735}
I0123 14:56:24.919061 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.053898003,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 0.62803006,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 73.00762,
'learning_rate': 0.03245735}
INFO:tensorflow:Step 71100 per-step time 0.406s
I0123 14:57:05.482297 140287364614016 model_lib_v2.py:707] Step 71100 per-step time 0.406s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0009091376,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 53.71598,
'Loss/RPNLoss/objectness_loss': 0.22815001,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 53.945038,
'learning_rate': 0.03225028}
I0123 14:57:05.482633 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0009091376,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 53.71598,
'Loss/RPNLoss/objectness_loss': 0.22815001,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 53.945038,
'learning_rate': 0.03225028}
INFO:tensorflow:Step 71200 per-step time 0.382s
I0123 14:57:43.635603 140287364614016 model_lib_v2.py:707] Step 71200 per-step time 0.382s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00051253196,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.4139921,
'Loss/RPNLoss/objectness_loss': 0.69627976,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 1.1107844,
'learning_rate': 0.032043703}
I0123 14:57:43.635941 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00051253196,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.4139921,
'Loss/RPNLoss/objectness_loss': 0.69627976,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 1.1107844,
'learning_rate': 0.032043703}
INFO:tensorflow:Step 71300 per-step time 0.375s
I0123 14:58:21.142415 140287364614016 model_lib_v2.py:707] Step 71300 per-step time 0.375s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00063346035,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 25.288792,
'Loss/RPNLoss/objectness_loss': 1.7235895,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 27.013014,
'learning_rate': 0.03183764}
I0123 14:58:21.142708 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00063346035,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 25.288792,
'Loss/RPNLoss/objectness_loss': 1.7235895,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 27.013014,
'learning_rate': 0.03183764}
INFO:tensorflow:Step 71400 per-step time 0.375s
I0123 14:58:58.675825 140287364614016 model_lib_v2.py:707] Step 71400 per-step time 0.375s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.12436466,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 105.56708,
'Loss/RPNLoss/objectness_loss': 1.8813133,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 107.572754,
'learning_rate': 0.031632062}
I0123 14:58:58.676180 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.12436466,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 105.56708,
'Loss/RPNLoss/objectness_loss': 1.8813133,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.031632062}
INFO:tensorflow:Step 71500 per-step time 0.380s
I0123 14:59:36.655242 140287364614016 model_lib_v2.py:707] Step 71500 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.9116488,
'Loss/BoxClassifierLoss/localization_loss': 0.8238957,
'Loss/RPNLoss/localization_loss': 105.023605,
'Loss/RPNLoss/objectness_loss': 0.4941777,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 107.25333,
'learning_rate': 0.03142699}
I0123 14:59:36.655540 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.9116488,
'Loss/BoxClassifierLoss/localization_loss': 0.8238957,
'Loss/RPNLoss/localization_loss': 105.023605,
'Loss/RPNLoss/objectness_loss': 0.4941777,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.03142699}
INFO:tensorflow:Step 71600 per-step time 0.383s
I0123 15:00:14.972617 140287364614016 model_lib_v2.py:707] Step 71600 per-step time 0.383s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.1618368,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 61.20216,
'Loss/RPNLoss/objectness_loss': 2.7929182,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 64.15691,
'learning_rate': 0.03122243}
I0123 15:00:14.972942 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.1618368,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 2.7929182,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.03122243}
INFO:tensorflow:Step 71700 per-step time 0.381s
I0123 15:00:53.110972 140287364614016 model_lib_v2.py:707] Step 71700 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.4398145,
'Loss/BoxClassifierLoss/localization_loss': 0.3579299,
'Loss/RPNLoss/localization_loss': 6.831536,
'Loss/RPNLoss/objectness_loss': 0.18332453,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 7.812605,
'learning_rate': 0.031018361}
I0123 15:00:53.111315 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.4398145,
'Loss/BoxClassifierLoss/localization_loss': 0.3579299,
'Loss/RPNLoss/localization_loss': 6.831536,
'Loss/RPNLoss/objectness_loss': 0.18332453,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.031018361}
INFO:tensorflow:Step 71800 per-step time 0.379s
I0123 15:01:31.024224 140287364614016 model_lib_v2.py:707] Step 71800 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.039415125,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 18.566307,
'Loss/RPNLoss/objectness_loss': 1.8501338,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 20.455856,
'learning_rate': 0.030814819}
I0123 15:01:31.024549 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.039415125,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 18.566307,
'Loss/RPNLoss/objectness_loss': 1.8501338,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.030814819}
INFO:tensorflow:Step 71900 per-step time 0.381s
I0123 15:02:09.087195 140287364614016 model_lib_v2.py:707] Step 71900 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.06524037,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 104.12228,
'Loss/RPNLoss/objectness_loss': 5.6834393,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 109.87096,
'learning_rate': 0.030611772}
I0123 15:02:09.087499 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.06524037,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 104.12228,
'Loss/RPNLoss/objectness_loss': 5.6834393,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.030611772}
INFO:tensorflow:Step 72000 per-step time 0.378s
I0123 15:02:46.920610 140287364614016 model_lib_v2.py:707] Step 72000 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.38183287,
'Loss/BoxClassifierLoss/localization_loss': 0.48898983,
'Loss/RPNLoss/localization_loss': 78.6706,
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'Loss/regularization_loss': 0.0,
'Loss/total_loss': 82.759415,
'learning_rate': 0.03040924}
I0123 15:02:46.920947 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.38183287,
'Loss/BoxClassifierLoss/localization_loss': 0.48898983,
'Loss/RPNLoss/localization_loss': 78.6706,
'Loss/RPNLoss/objectness_loss': 3.2179904,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 82.759415,
'learning_rate': 0.03040924}
INFO:tensorflow:Step 72100 per-step time 0.406s
I0123 15:03:27.554902 140287364614016 model_lib_v2.py:707] Step 72100 per-step time 0.406s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00082473876,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 88.6578,
'Loss/RPNLoss/objectness_loss': 0.47007427,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 89.1287,
'learning_rate': 0.030207219}
I0123 15:03:27.555239 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00082473876,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 88.6578,
'Loss/RPNLoss/objectness_loss': 0.47007427,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 89.1287,
'learning_rate': 0.030207219}
INFO:tensorflow:Step 72200 per-step time 0.377s
I0123 15:04:05.281454 140287364614016 model_lib_v2.py:707] Step 72200 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.011326542,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 99.24808,
'Loss/RPNLoss/objectness_loss': 2.7507653,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 102.01017,
'learning_rate': 0.03000573}
I0123 15:04:05.281760 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.011326542,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 99.24808,
'Loss/RPNLoss/objectness_loss': 2.7507653,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 102.01017,
'learning_rate': 0.03000573}
INFO:tensorflow:Step 72300 per-step time 0.375s
I0123 15:04:42.763705 140287364614016 model_lib_v2.py:707] Step 72300 per-step time 0.375s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.000346513,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 7.0507717,
'Loss/RPNLoss/objectness_loss': 1.4530802,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 8.504198,
'learning_rate': 0.02980475}
I0123 15:04:42.764028 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.000346513,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 7.0507717,
'Loss/RPNLoss/objectness_loss': 1.4530802,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 8.504198,
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INFO:tensorflow:Step 72400 per-step time 0.375s
I0123 15:05:20.271388 140287364614016 model_lib_v2.py:707] Step 72400 per-step time 0.375s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.026922105,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.29329443,
'Loss/RPNLoss/objectness_loss': 1.9307644,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 2.2509809,
'learning_rate': 0.0296043}
I0123 15:05:20.271713 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.026922105,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.29329443,
'Loss/RPNLoss/objectness_loss': 1.9307644,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 2.2509809,
'learning_rate': 0.0296043}
INFO:tensorflow:Step 72500 per-step time 0.378s
I0123 15:05:58.045071 140287364614016 model_lib_v2.py:707] Step 72500 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.3987422,
'Loss/BoxClassifierLoss/localization_loss': 0.6076676,
'Loss/RPNLoss/localization_loss': 169.30276,
'Loss/RPNLoss/objectness_loss': 1.582552,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 172.89172,
'learning_rate': 0.029404363}
I0123 15:05:58.045361 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.3987422,
'Loss/BoxClassifierLoss/localization_loss': 0.6076676,
'Loss/RPNLoss/localization_loss': 169.30276,
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'Loss/regularization_loss': 0.0,
'Loss/total_loss': 172.89172,
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INFO:tensorflow:Step 72600 per-step time 0.382s
I0123 15:06:36.228900 140287364614016 model_lib_v2.py:707] Step 72600 per-step time 0.382s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.29784897,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 3.8476028,
'Loss/RPNLoss/objectness_loss': 3.1605659,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 7.306018,
'learning_rate': 0.02920495}
I0123 15:06:36.229202 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.29784897,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 3.1605659,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.02920495}
INFO:tensorflow:Step 72700 per-step time 0.383s
I0123 15:07:14.501091 140287364614016 model_lib_v2.py:707] Step 72700 per-step time 0.383s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.012026801,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 12.4285,
'Loss/RPNLoss/objectness_loss': 0.22433713,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 12.664865,
'learning_rate': 0.02900608}
I0123 15:07:14.501429 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.012026801,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 12.4285,
'Loss/RPNLoss/objectness_loss': 0.22433713,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 12.664865,
'learning_rate': 0.02900608}
INFO:tensorflow:Step 72800 per-step time 0.381s
I0123 15:07:52.568845 140287364614016 model_lib_v2.py:707] Step 72800 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0021170634,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 1.0506619,
'Loss/RPNLoss/objectness_loss': 0.076780505,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 1.1295595,
'learning_rate': 0.028807726}
I0123 15:07:52.569163 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0021170634,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 0.076780505,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.028807726}
INFO:tensorflow:Step 72900 per-step time 0.384s
I0123 15:08:30.936770 140287364614016 model_lib_v2.py:707] Step 72900 per-step time 0.384s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.1757136,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 53.46627,
'Loss/RPNLoss/objectness_loss': 0.74252445,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 54.38451,
'learning_rate': 0.0286099}
I0123 15:08:30.937078 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.1757136,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 53.46627,
'Loss/RPNLoss/objectness_loss': 0.74252445,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.0286099}
INFO:tensorflow:Step 73000 per-step time 0.378s
I0123 15:09:08.711704 140287364614016 model_lib_v2.py:707] Step 73000 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0067836014,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.33196327,
'Loss/RPNLoss/objectness_loss': 2.36171,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 2.7004569,
'learning_rate': 0.028412623}
I0123 15:09:08.712028 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0067836014,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.33196327,
'Loss/RPNLoss/objectness_loss': 2.36171,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.028412623}
INFO:tensorflow:Step 73100 per-step time 0.413s
I0123 15:09:50.024610 140287364614016 model_lib_v2.py:707] Step 73100 per-step time 0.413s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0009526922,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 73.465996,
'Loss/RPNLoss/objectness_loss': 0.33659932,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 73.80355,
'learning_rate': 0.028215865}
I0123 15:09:50.024907 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0009526922,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 73.465996,
'Loss/RPNLoss/objectness_loss': 0.33659932,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.028215865}
INFO:tensorflow:Step 73200 per-step time 0.383s
I0123 15:10:28.358498 140287364614016 model_lib_v2.py:707] Step 73200 per-step time 0.383s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.24346104,
'Loss/BoxClassifierLoss/localization_loss': 0.102367304,
'Loss/RPNLoss/localization_loss': 0.470524,
'Loss/RPNLoss/objectness_loss': 6.8945785,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 7.710931,
'learning_rate': 0.028019646}
I0123 15:10:28.358799 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.24346104,
'Loss/BoxClassifierLoss/localization_loss': 0.102367304,
'Loss/RPNLoss/localization_loss': 0.470524,
'Loss/RPNLoss/objectness_loss': 6.8945785,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.028019646}
INFO:tensorflow:Step 73300 per-step time 0.379s
I0123 15:11:06.283710 140287364614016 model_lib_v2.py:707] Step 73300 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.7973008,
'Loss/BoxClassifierLoss/localization_loss': 0.9135337,
'Loss/RPNLoss/localization_loss': 94.00036,
'Loss/RPNLoss/objectness_loss': 4.513314,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 100.22451,
'learning_rate': 0.02782398}
I0123 15:11:06.284035 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.7973008,
'Loss/BoxClassifierLoss/localization_loss': 0.9135337,
'Loss/RPNLoss/localization_loss': 94.00036,
'Loss/RPNLoss/objectness_loss': 4.513314,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 100.22451,
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INFO:tensorflow:Step 73400 per-step time 0.380s
I0123 15:11:44.279416 140287364614016 model_lib_v2.py:707] Step 73400 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.3891243,
'Loss/BoxClassifierLoss/localization_loss': 0.8228241,
'Loss/RPNLoss/localization_loss': 127.63491,
'Loss/RPNLoss/objectness_loss': 0.6003491,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 130.4472,
'learning_rate': 0.027628846}
I0123 15:11:44.279750 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.3891243,
'Loss/BoxClassifierLoss/localization_loss': 0.8228241,
'Loss/RPNLoss/localization_loss': 127.63491,
'Loss/RPNLoss/objectness_loss': 0.6003491,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 130.4472,
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INFO:tensorflow:Step 73500 per-step time 0.377s
I0123 15:12:21.947611 140287364614016 model_lib_v2.py:707] Step 73500 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00087223866,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 2.0859091,
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'Loss/regularization_loss': 0.0,
'Loss/total_loss': 3.269761,
'learning_rate': 0.027434267}
I0123 15:12:21.947959 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00087223866,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 1.1829796,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.027434267}
INFO:tensorflow:Step 73600 per-step time 0.382s
I0123 15:13:00.182371 140287364614016 model_lib_v2.py:707] Step 73600 per-step time 0.382s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.014166585,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 160.85037,
'Loss/RPNLoss/objectness_loss': 0.17536244,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 161.0399,
'learning_rate': 0.02724021}
I0123 15:13:00.182695 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.014166585,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 160.85037,
'Loss/RPNLoss/objectness_loss': 0.17536244,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 161.0399,
'learning_rate': 0.02724021}
INFO:tensorflow:Step 73700 per-step time 0.379s
I0123 15:13:38.050254 140287364614016 model_lib_v2.py:707] Step 73700 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00043680478,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 1.2637485,
'Loss/RPNLoss/objectness_loss': 0.7873463,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 2.0515316,
'learning_rate': 0.027046723}
I0123 15:13:38.050566 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00043680478,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 0.7873463,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.027046723}
INFO:tensorflow:Step 73800 per-step time 0.376s
I0123 15:14:15.659247 140287364614016 model_lib_v2.py:707] Step 73800 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0518392,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.24848199,
'Loss/RPNLoss/objectness_loss': 5.452464,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 5.7527857,
'learning_rate': 0.02685377}
I0123 15:14:15.659525 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0518392,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 5.452464,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.02685377}
INFO:tensorflow:Step 73900 per-step time 0.372s
I0123 15:14:52.842096 140287364614016 model_lib_v2.py:707] Step 73900 per-step time 0.372s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00036071072,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 132.65652,
'Loss/RPNLoss/objectness_loss': 1.7085826,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 134.36546,
'learning_rate': 0.02666139}
I0123 15:14:52.842406 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00036071072,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 132.65652,
'Loss/RPNLoss/objectness_loss': 1.7085826,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 134.36546,
'learning_rate': 0.02666139}
INFO:tensorflow:Step 74000 per-step time 0.374s
I0123 15:15:30.273367 140287364614016 model_lib_v2.py:707] Step 74000 per-step time 0.374s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0051634186,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 79.47951,
'Loss/RPNLoss/objectness_loss': 1.3588843,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 80.84355,
'learning_rate': 0.026469545}
I0123 15:15:30.273694 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0051634186,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 79.47951,
'Loss/RPNLoss/objectness_loss': 1.3588843,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 80.84355,
'learning_rate': 0.026469545}
INFO:tensorflow:Step 74100 per-step time 0.414s
I0123 15:16:11.641824 140287364614016 model_lib_v2.py:707] Step 74100 per-step time 0.414s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.5315701,
'Loss/BoxClassifierLoss/localization_loss': 1.0494521,
'Loss/RPNLoss/localization_loss': 81.81295,
'Loss/RPNLoss/objectness_loss': 0.74107355,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 85.13505,
'learning_rate': 0.026278257}
I0123 15:16:11.642187 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.5315701,
'Loss/BoxClassifierLoss/localization_loss': 1.0494521,
'Loss/RPNLoss/localization_loss': 81.81295,
'Loss/RPNLoss/objectness_loss': 0.74107355,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.026278257}
INFO:tensorflow:Step 74200 per-step time 0.382s
I0123 15:16:49.830206 140287364614016 model_lib_v2.py:707] Step 74200 per-step time 0.382s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.02313353,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 122.058174,
'Loss/RPNLoss/objectness_loss': 1.8267165,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 123.90803,
'learning_rate': 0.026087541}
I0123 15:16:49.830522 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.02313353,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 122.058174,
'Loss/RPNLoss/objectness_loss': 1.8267165,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.026087541}
INFO:tensorflow:Step 74300 per-step time 0.378s
I0123 15:17:27.606276 140287364614016 model_lib_v2.py:707] Step 74300 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.69090396,
'Loss/BoxClassifierLoss/localization_loss': 0.1614978,
'Loss/RPNLoss/localization_loss': 139.08557,
'Loss/RPNLoss/objectness_loss': 0.5221657,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 140.46014,
'learning_rate': 0.025897369}
I0123 15:17:27.606603 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.69090396,
'Loss/BoxClassifierLoss/localization_loss': 0.1614978,
'Loss/RPNLoss/localization_loss': 139.08557,
'Loss/RPNLoss/objectness_loss': 0.5221657,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 140.46014,
'learning_rate': 0.025897369}
INFO:tensorflow:Step 74400 per-step time 0.381s
I0123 15:18:05.679470 140287364614016 model_lib_v2.py:707] Step 74400 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.58643967,
'Loss/BoxClassifierLoss/localization_loss': 0.6513663,
'Loss/RPNLoss/localization_loss': 81.93197,
'Loss/RPNLoss/objectness_loss': 0.45067203,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 83.620445,
'learning_rate': 0.025707759}
I0123 15:18:05.679760 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.58643967,
'Loss/BoxClassifierLoss/localization_loss': 0.6513663,
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'Loss/RPNLoss/objectness_loss': 0.45067203,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.025707759}
INFO:tensorflow:Step 74500 per-step time 0.381s
I0123 15:18:43.730024 140287364614016 model_lib_v2.py:707] Step 74500 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00080542394,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 116.04094,
'Loss/RPNLoss/objectness_loss': 1.9837451,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 118.02549,
'learning_rate': 0.025518727}
I0123 15:18:43.730333 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00080542394,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 116.04094,
'Loss/RPNLoss/objectness_loss': 1.9837451,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.025518727}
INFO:tensorflow:Step 74600 per-step time 0.380s
I0123 15:19:21.683789 140287364614016 model_lib_v2.py:707] Step 74600 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0037135042,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 101.23477,
'Loss/RPNLoss/objectness_loss': 3.608337,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 104.846825,
'learning_rate': 0.025330247}
I0123 15:19:21.684133 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0037135042,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 3.608337,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 104.846825,
'learning_rate': 0.025330247}
INFO:tensorflow:Step 74700 per-step time 0.379s
I0123 15:19:59.610596 140287364614016 model_lib_v2.py:707] Step 74700 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.003726949,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 66.91364,
'Loss/RPNLoss/objectness_loss': 2.1933343,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 69.1107,
'learning_rate': 0.025142334}
I0123 15:19:59.610936 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.003726949,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 66.91364,
'Loss/RPNLoss/objectness_loss': 2.1933343,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 69.1107,
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INFO:tensorflow:Step 74800 per-step time 0.384s
I0123 15:20:37.979602 140287364614016 model_lib_v2.py:707] Step 74800 per-step time 0.384s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.3525428,
'Loss/BoxClassifierLoss/localization_loss': 0.49425453,
'Loss/RPNLoss/localization_loss': 41.610542,
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'Loss/regularization_loss': 0.0,
'Loss/total_loss': 44.26864,
'learning_rate': 0.02495501}
I0123 15:20:37.979953 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.3525428,
'Loss/BoxClassifierLoss/localization_loss': 0.49425453,
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'Loss/RPNLoss/objectness_loss': 0.811298,
'Loss/regularization_loss': 0.0,
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INFO:tensorflow:Step 74900 per-step time 0.379s
I0123 15:21:15.877475 140287364614016 model_lib_v2.py:707] Step 74900 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0005020907,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 31.087315,
'Loss/RPNLoss/objectness_loss': 0.96646076,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 32.05428,
'learning_rate': 0.024768237}
I0123 15:21:15.877801 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0005020907,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 0.96646076,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.024768237}
INFO:tensorflow:Step 75000 per-step time 0.378s
I0123 15:21:53.708402 140287364614016 model_lib_v2.py:707] Step 75000 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 2.0565448,
'Loss/BoxClassifierLoss/localization_loss': 1.534173,
'Loss/RPNLoss/localization_loss': 21.930069,
'Loss/RPNLoss/objectness_loss': 0.49400648,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.024582053}
I0123 15:21:53.708717 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 2.0565448,
'Loss/BoxClassifierLoss/localization_loss': 1.534173,
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'Loss/RPNLoss/objectness_loss': 0.49400648,
'Loss/regularization_loss': 0.0,
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INFO:tensorflow:Step 75100 per-step time 0.411s
I0123 15:22:34.848655 140287364614016 model_lib_v2.py:707] Step 75100 per-step time 0.411s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0030693708,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 102.17622,
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'Loss/regularization_loss': 0.0,
'Loss/total_loss': 102.5969,
'learning_rate': 0.02439642}
I0123 15:22:34.848964 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0030693708,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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INFO:tensorflow:Step 75200 per-step time 0.387s
I0123 15:23:13.514364 140287364614016 model_lib_v2.py:707] Step 75200 per-step time 0.387s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.3465189,
'Loss/BoxClassifierLoss/localization_loss': 0.30124843,
'Loss/RPNLoss/localization_loss': 116.858185,
'Loss/RPNLoss/objectness_loss': 1.5563141,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 120.06226,
'learning_rate': 0.024211386}
I0123 15:23:13.514681 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.3465189,
'Loss/BoxClassifierLoss/localization_loss': 0.30124843,
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'Loss/regularization_loss': 0.0,
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INFO:tensorflow:Step 75300 per-step time 0.380s
I0123 15:23:51.480871 140287364614016 model_lib_v2.py:707] Step 75300 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00086909154,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 64.00852,
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'learning_rate': 0.024026923}
I0123 15:23:51.481171 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00086909154,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 0.23152813,
'Loss/regularization_loss': 0.0,
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INFO:tensorflow:Step 75400 per-step time 0.379s
I0123 15:24:29.338495 140287364614016 model_lib_v2.py:707] Step 75400 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.6073918,
'Loss/BoxClassifierLoss/localization_loss': 1.1470118,
'Loss/RPNLoss/localization_loss': 18.412388,
'Loss/RPNLoss/objectness_loss': 0.15155664,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 21.318348,
'learning_rate': 0.02384305}
I0123 15:24:29.338837 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.6073918,
'Loss/BoxClassifierLoss/localization_loss': 1.1470118,
'Loss/RPNLoss/localization_loss': 18.412388,
'Loss/RPNLoss/objectness_loss': 0.15155664,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.02384305}
INFO:tensorflow:Step 75500 per-step time 0.377s
I0123 15:25:07.035294 140287364614016 model_lib_v2.py:707] Step 75500 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00036887912,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 175.6591,
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'Loss/regularization_loss': 0.0,
'Loss/total_loss': 175.95396,
'learning_rate': 0.023659749}
I0123 15:25:07.035643 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00036887912,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 0.29448605,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.023659749}
INFO:tensorflow:Step 75600 per-step time 0.379s
I0123 15:25:44.893082 140287364614016 model_lib_v2.py:707] Step 75600 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0005750695,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.057603806,
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'learning_rate': 0.023477048}
I0123 15:25:44.893386 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0005750695,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 0.554935,
'Loss/regularization_loss': 0.0,
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INFO:tensorflow:Step 75700 per-step time 0.380s
I0123 15:26:22.903502 140287364614016 model_lib_v2.py:707] Step 75700 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.6722314,
'Loss/BoxClassifierLoss/localization_loss': 0.9127158,
'Loss/RPNLoss/localization_loss': 38.15146,
'Loss/RPNLoss/objectness_loss': 0.24437612,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 40.98078,
'learning_rate': 0.023294916}
I0123 15:26:22.903827 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.6722314,
'Loss/BoxClassifierLoss/localization_loss': 0.9127158,
'Loss/RPNLoss/localization_loss': 38.15146,
'Loss/RPNLoss/objectness_loss': 0.24437612,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 40.98078,
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INFO:tensorflow:Step 75800 per-step time 0.381s
I0123 15:27:01.018352 140287364614016 model_lib_v2.py:707] Step 75800 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.008517849,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 4.389163,
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'Loss/regularization_loss': 0.0,
'Loss/total_loss': 5.0443687,
'learning_rate': 0.02311338}
I0123 15:27:01.018673 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.008517849,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 0.64668775,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.02311338}
INFO:tensorflow:Step 75900 per-step time 0.380s
I0123 15:27:38.978788 140287364614016 model_lib_v2.py:707] Step 75900 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00030544394,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 84.35978,
'Loss/RPNLoss/objectness_loss': 0.88528794,
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'Loss/total_loss': 85.24537,
'learning_rate': 0.022932423}
I0123 15:27:38.979098 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00030544394,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 0.88528794,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.022932423}
INFO:tensorflow:Step 76000 per-step time 0.380s
I0123 15:28:16.993444 140287364614016 model_lib_v2.py:707] Step 76000 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00058677385,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 1.2238488,
'Loss/RPNLoss/objectness_loss': 0.96406686,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 2.1885023,
'learning_rate': 0.022752084}
I0123 15:28:16.993769 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00058677385,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 0.96406686,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 2.1885023,
'learning_rate': 0.022752084}
INFO:tensorflow:Step 76100 per-step time 0.417s
I0123 15:28:58.720440 140287364614016 model_lib_v2.py:707] Step 76100 per-step time 0.417s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0022513312,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.5896965,
'Loss/RPNLoss/objectness_loss': 0.6939055,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 1.2858534,
'learning_rate': 0.022572322}
I0123 15:28:58.720772 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0022513312,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 0.6939055,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.022572322}
INFO:tensorflow:Step 76200 per-step time 0.381s
I0123 15:29:36.830358 140287364614016 model_lib_v2.py:707] Step 76200 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.018996641,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 66.43071,
'Loss/RPNLoss/objectness_loss': 0.87012744,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 67.31983,
'learning_rate': 0.02239315}
I0123 15:29:36.830646 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.018996641,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 0.87012744,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.02239315}
INFO:tensorflow:Step 76300 per-step time 0.378s
I0123 15:30:14.626202 140287364614016 model_lib_v2.py:707] Step 76300 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00031364983,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 58.800232,
'Loss/RPNLoss/objectness_loss': 0.8350105,
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'Loss/total_loss': 59.635555,
'learning_rate': 0.022214599}
I0123 15:30:14.626487 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00031364983,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 0.8350105,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.022214599}
INFO:tensorflow:Step 76400 per-step time 0.382s
I0123 15:30:52.808140 140287364614016 model_lib_v2.py:707] Step 76400 per-step time 0.382s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.07520822,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 67.06554,
'Loss/RPNLoss/objectness_loss': 0.8602175,
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'Loss/total_loss': 68.00096,
'learning_rate': 0.022036623}
I0123 15:30:52.808469 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.07520822,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 0.8602175,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.022036623}
INFO:tensorflow:Step 76500 per-step time 0.383s
I0123 15:31:31.062405 140287364614016 model_lib_v2.py:707] Step 76500 per-step time 0.383s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0013287999,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 56.67209,
'Loss/RPNLoss/objectness_loss': 1.6380124,
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'learning_rate': 0.021859273}
I0123 15:31:31.062715 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0013287999,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 1.6380124,
'Loss/regularization_loss': 0.0,
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INFO:tensorflow:Step 76600 per-step time 0.376s
I0123 15:32:08.679230 140287364614016 model_lib_v2.py:707] Step 76600 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.8231729,
'Loss/BoxClassifierLoss/localization_loss': 1.0577002,
'Loss/RPNLoss/localization_loss': 0.13789749,
'Loss/RPNLoss/objectness_loss': 1.1101811,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 4.128952,
'learning_rate': 0.02168251}
I0123 15:32:08.679522 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.8231729,
'Loss/BoxClassifierLoss/localization_loss': 1.0577002,
'Loss/RPNLoss/localization_loss': 0.13789749,
'Loss/RPNLoss/objectness_loss': 1.1101811,
'Loss/regularization_loss': 0.0,
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INFO:tensorflow:Step 76700 per-step time 0.382s
I0123 15:32:46.856561 140287364614016 model_lib_v2.py:707] Step 76700 per-step time 0.382s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0012362511,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 5.1113415,
'Loss/RPNLoss/objectness_loss': 0.67847073,
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'learning_rate': 0.021506352}
I0123 15:32:46.856862 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0012362511,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/RPNLoss/objectness_loss': 0.67847073,
'Loss/regularization_loss': 0.0,
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'learning_rate': 0.021506352}
INFO:tensorflow:Step 76800 per-step time 0.377s
I0123 15:33:24.587571 140287364614016 model_lib_v2.py:707] Step 76800 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00025268336,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 1.7412455,
'Loss/RPNLoss/objectness_loss': 1.0087774,
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'learning_rate': 0.0213308}
I0123 15:33:24.587913 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00025268336,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
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'Loss/total_loss': 2.7502756,
'learning_rate': 0.0213308}
INFO:tensorflow:Step 76900 per-step time 0.380s
I0123 15:34:02.624104 140287364614016 model_lib_v2.py:707] Step 76900 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0030012187,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 13.331156,
'Loss/RPNLoss/objectness_loss': 0.12604812,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 13.460205,
'learning_rate': 0.021155871}
I0123 15:34:02.624394 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0030012187,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 13.331156,
'Loss/RPNLoss/objectness_loss': 0.12604812,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 13.460205,
'learning_rate': 0.021155871}
INFO:tensorflow:Step 77000 per-step time 0.379s
I0123 15:34:40.532523 140287364614016 model_lib_v2.py:707] Step 77000 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.0919218,
'Loss/BoxClassifierLoss/localization_loss': 0.140659,
'Loss/RPNLoss/localization_loss': 47.101585,
'Loss/RPNLoss/objectness_loss': 0.22491439,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 48.559082,
'learning_rate': 0.02098154}
I0123 15:34:40.532835 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.0919218,
'Loss/BoxClassifierLoss/localization_loss': 0.140659,
'Loss/RPNLoss/localization_loss': 47.101585,
'Loss/RPNLoss/objectness_loss': 0.22491439,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 48.559082,
'learning_rate': 0.02098154}
INFO:tensorflow:Step 77100 per-step time 0.412s
I0123 15:35:21.712686 140287364614016 model_lib_v2.py:707] Step 77100 per-step time 0.412s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00040932227,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 3.0656824,
'Loss/RPNLoss/objectness_loss': 0.81322575,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 3.8793175,
'learning_rate': 0.020807832}
I0123 15:35:21.713036 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00040932227,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 3.0656824,
'Loss/RPNLoss/objectness_loss': 0.81322575,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 3.8793175,
'learning_rate': 0.020807832}
INFO:tensorflow:Step 77200 per-step time 0.380s
I0123 15:35:59.719931 140287364614016 model_lib_v2.py:707] Step 77200 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0003581087,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 2.5048707,
'Loss/RPNLoss/objectness_loss': 1.4491222,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 3.954351,
'learning_rate': 0.020634731}
I0123 15:35:59.720236 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0003581087,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 2.5048707,
'Loss/RPNLoss/objectness_loss': 1.4491222,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 3.954351,
'learning_rate': 0.020634731}
INFO:tensorflow:Step 77300 per-step time 0.380s
I0123 15:36:37.753147 140287364614016 model_lib_v2.py:707] Step 77300 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00035074557,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.5065867,
'Loss/RPNLoss/objectness_loss': 2.1220837,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 2.6290212,
'learning_rate': 0.020462245}
I0123 15:36:37.753439 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00035074557,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.5065867,
'Loss/RPNLoss/objectness_loss': 2.1220837,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 2.6290212,
'learning_rate': 0.020462245}
INFO:tensorflow:Step 77400 per-step time 0.377s
I0123 15:37:15.425751 140287364614016 model_lib_v2.py:707] Step 77400 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.08561217,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.38058013,
'Loss/RPNLoss/objectness_loss': 1.6345601,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 2.1007524,
'learning_rate': 0.020290375}
I0123 15:37:15.426166 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.08561217,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.38058013,
'Loss/RPNLoss/objectness_loss': 1.6345601,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 2.1007524,
'learning_rate': 0.020290375}
INFO:tensorflow:Step 77500 per-step time 0.377s
I0123 15:37:53.174696 140287364614016 model_lib_v2.py:707] Step 77500 per-step time 0.377s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.25791025,
'Loss/BoxClassifierLoss/localization_loss': 0.06800346,
'Loss/RPNLoss/localization_loss': 48.177216,
'Loss/RPNLoss/objectness_loss': 0.5806861,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 49.083817,
'learning_rate': 0.020119138}
I0123 15:37:53.175018 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.25791025,
'Loss/BoxClassifierLoss/localization_loss': 0.06800346,
'Loss/RPNLoss/localization_loss': 48.177216,
'Loss/RPNLoss/objectness_loss': 0.5806861,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 49.083817,
'learning_rate': 0.020119138}
INFO:tensorflow:Step 77600 per-step time 0.380s
I0123 15:38:31.187711 140287364614016 model_lib_v2.py:707] Step 77600 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0043492774,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 70.53496,
'Loss/RPNLoss/objectness_loss': 1.3098918,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 71.8492,
'learning_rate': 0.01994851}
I0123 15:38:31.188009 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0043492774,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 70.53496,
'Loss/RPNLoss/objectness_loss': 1.3098918,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 71.8492,
'learning_rate': 0.01994851}
INFO:tensorflow:Step 77700 per-step time 0.381s
I0123 15:39:09.246784 140287364614016 model_lib_v2.py:707] Step 77700 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.003981524,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 21.39299,
'Loss/RPNLoss/objectness_loss': 4.3864145,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 25.783386,
'learning_rate': 0.019778503}
I0123 15:39:09.247107 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.003981524,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 21.39299,
'Loss/RPNLoss/objectness_loss': 4.3864145,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 25.783386,
'learning_rate': 0.019778503}
INFO:tensorflow:Step 77800 per-step time 0.380s
I0123 15:39:47.234484 140287364614016 model_lib_v2.py:707] Step 77800 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.023932254,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 2.945105,
'Loss/RPNLoss/objectness_loss': 1.521908,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 4.4909453,
'learning_rate': 0.01960914}
I0123 15:39:47.234776 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.023932254,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 2.945105,
'Loss/RPNLoss/objectness_loss': 1.521908,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 4.4909453,
'learning_rate': 0.01960914}
INFO:tensorflow:Step 77900 per-step time 0.380s
I0123 15:40:25.224005 140287364614016 model_lib_v2.py:707] Step 77900 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0051976857,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 19.76358,
'Loss/RPNLoss/objectness_loss': 0.28949046,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 20.058268,
'learning_rate': 0.019440388}
I0123 15:40:25.224303 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0051976857,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 19.76358,
'Loss/RPNLoss/objectness_loss': 0.28949046,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 20.058268,
'learning_rate': 0.019440388}
INFO:tensorflow:Step 78000 per-step time 0.383s
I0123 15:41:03.494305 140287364614016 model_lib_v2.py:707] Step 78000 per-step time 0.383s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.10988955,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 2.9954338,
'Loss/RPNLoss/objectness_loss': 1.735971,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 4.8412943,
'learning_rate': 0.019272279}
I0123 15:41:03.494628 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.10988955,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 2.9954338,
'Loss/RPNLoss/objectness_loss': 1.735971,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 4.8412943,
'learning_rate': 0.019272279}
INFO:tensorflow:Step 78100 per-step time 0.419s
I0123 15:41:45.384567 140287364614016 model_lib_v2.py:707] Step 78100 per-step time 0.419s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00046214278,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 53.931183,
'Loss/RPNLoss/objectness_loss': 0.22064924,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 54.152294,
'learning_rate': 0.019104786}
I0123 15:41:45.384884 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00046214278,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 53.931183,
'Loss/RPNLoss/objectness_loss': 0.22064924,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 54.152294,
'learning_rate': 0.019104786}
INFO:tensorflow:Step 78200 per-step time 0.382s
I0123 15:42:23.611041 140287364614016 model_lib_v2.py:707] Step 78200 per-step time 0.382s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0039063143,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 54.510292,
'Loss/RPNLoss/objectness_loss': 2.6017735,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 57.11597,
'learning_rate': 0.018937927}
I0123 15:42:23.611338 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0039063143,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 54.510292,
'Loss/RPNLoss/objectness_loss': 2.6017735,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 57.11597,
'learning_rate': 0.018937927}
INFO:tensorflow:Step 78300 per-step time 0.381s
I0123 15:43:01.742781 140287364614016 model_lib_v2.py:707] Step 78300 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.68273747,
'Loss/BoxClassifierLoss/localization_loss': 0.5587101,
'Loss/RPNLoss/localization_loss': 51.242477,
'Loss/RPNLoss/objectness_loss': 0.60192883,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 53.085854,
'learning_rate': 0.0187717}
I0123 15:43:01.743110 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.68273747,
'Loss/BoxClassifierLoss/localization_loss': 0.5587101,
'Loss/RPNLoss/localization_loss': 51.242477,
'Loss/RPNLoss/objectness_loss': 0.60192883,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 53.085854,
'learning_rate': 0.0187717}
INFO:tensorflow:Step 78400 per-step time 0.378s
I0123 15:43:39.522013 140287364614016 model_lib_v2.py:707] Step 78400 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.5489243,
'Loss/BoxClassifierLoss/localization_loss': 0.9277016,
'Loss/RPNLoss/localization_loss': 10.535217,
'Loss/RPNLoss/objectness_loss': 0.45664868,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 13.468492,
'learning_rate': 0.018606123}
I0123 15:43:39.522307 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.5489243,
'Loss/BoxClassifierLoss/localization_loss': 0.9277016,
'Loss/RPNLoss/localization_loss': 10.535217,
'Loss/RPNLoss/objectness_loss': 0.45664868,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 13.468492,
'learning_rate': 0.018606123}
INFO:tensorflow:Step 78500 per-step time 0.378s
I0123 15:44:17.358415 140287364614016 model_lib_v2.py:707] Step 78500 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.008372214,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 92.36749,
'Loss/RPNLoss/objectness_loss': 2.988128,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 95.36399,
'learning_rate': 0.01844117}
I0123 15:44:17.358734 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.008372214,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 92.36749,
'Loss/RPNLoss/objectness_loss': 2.988128,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 95.36399,
'learning_rate': 0.01844117}
INFO:tensorflow:Step 78600 per-step time 0.382s
I0123 15:44:55.526202 140287364614016 model_lib_v2.py:707] Step 78600 per-step time 0.382s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.1489487,
'Loss/BoxClassifierLoss/localization_loss': 0.42087767,
'Loss/RPNLoss/localization_loss': 36.483093,
'Loss/RPNLoss/objectness_loss': 0.48677328,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 38.539692,
'learning_rate': 0.018276868}
I0123 15:44:55.526542 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.1489487,
'Loss/BoxClassifierLoss/localization_loss': 0.42087767,
'Loss/RPNLoss/localization_loss': 36.483093,
'Loss/RPNLoss/objectness_loss': 0.48677328,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 38.539692,
'learning_rate': 0.018276868}
INFO:tensorflow:Step 78700 per-step time 0.380s
I0123 15:45:33.569119 140287364614016 model_lib_v2.py:707] Step 78700 per-step time 0.380s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.50374544,
'Loss/BoxClassifierLoss/localization_loss': 0.2797015,
'Loss/RPNLoss/localization_loss': 36.63706,
'Loss/RPNLoss/objectness_loss': 0.5933877,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 38.013893,
'learning_rate': 0.018113194}
I0123 15:45:33.569411 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.50374544,
'Loss/BoxClassifierLoss/localization_loss': 0.2797015,
'Loss/RPNLoss/localization_loss': 36.63706,
'Loss/RPNLoss/objectness_loss': 0.5933877,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 38.013893,
'learning_rate': 0.018113194}
INFO:tensorflow:Step 78800 per-step time 0.376s
I0123 15:46:11.132283 140287364614016 model_lib_v2.py:707] Step 78800 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.05237865,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 55.568527,
'Loss/RPNLoss/objectness_loss': 0.33276504,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 55.95367,
'learning_rate': 0.017950162}
I0123 15:46:11.132573 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.05237865,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 55.568527,
'Loss/RPNLoss/objectness_loss': 0.33276504,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 55.95367,
'learning_rate': 0.017950162}
INFO:tensorflow:Step 78900 per-step time 0.381s
I0123 15:46:49.271357 140287364614016 model_lib_v2.py:707] Step 78900 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.025114423,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.16968846,
'Loss/RPNLoss/objectness_loss': 0.52933884,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 0.7241417,
'learning_rate': 0.017787775}
I0123 15:46:49.271667 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.025114423,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.16968846,
'Loss/RPNLoss/objectness_loss': 0.52933884,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 0.7241417,
'learning_rate': 0.017787775}
INFO:tensorflow:Step 79000 per-step time 0.379s
I0123 15:47:27.190949 140287364614016 model_lib_v2.py:707] Step 79000 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.0853711,
'Loss/BoxClassifierLoss/localization_loss': 0.66082025,
'Loss/RPNLoss/localization_loss': 0.3477788,
'Loss/RPNLoss/objectness_loss': 1.6710162,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 3.7649865,
'learning_rate': 0.017626047}
I0123 15:47:27.191260 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.0853711,
'Loss/BoxClassifierLoss/localization_loss': 0.66082025,
'Loss/RPNLoss/localization_loss': 0.3477788,
'Loss/RPNLoss/objectness_loss': 1.6710162,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 3.7649865,
'learning_rate': 0.017626047}
INFO:tensorflow:Step 79100 per-step time 0.424s
I0123 15:48:09.570188 140287364614016 model_lib_v2.py:707] Step 79100 per-step time 0.424s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.2475022,
'Loss/BoxClassifierLoss/localization_loss': 1.8867043,
'Loss/RPNLoss/localization_loss': 71.772606,
'Loss/RPNLoss/objectness_loss': 1.4518687,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 76.35868,
'learning_rate': 0.017464956}
I0123 15:48:09.570483 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.2475022,
'Loss/BoxClassifierLoss/localization_loss': 1.8867043,
'Loss/RPNLoss/localization_loss': 71.772606,
'Loss/RPNLoss/objectness_loss': 1.4518687,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 76.35868,
'learning_rate': 0.017464956}
INFO:tensorflow:Step 79200 per-step time 0.381s
I0123 15:48:47.700515 140287364614016 model_lib_v2.py:707] Step 79200 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.6267061,
'Loss/BoxClassifierLoss/localization_loss': 1.695502,
'Loss/RPNLoss/localization_loss': 1.3945422,
'Loss/RPNLoss/objectness_loss': 1.3122969,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 6.0290475,
'learning_rate': 0.017304525}
I0123 15:48:47.700846 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.6267061,
'Loss/BoxClassifierLoss/localization_loss': 1.695502,
'Loss/RPNLoss/localization_loss': 1.3945422,
'Loss/RPNLoss/objectness_loss': 1.3122969,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 6.0290475,
'learning_rate': 0.017304525}
INFO:tensorflow:Step 79300 per-step time 0.382s
I0123 15:49:25.945189 140287364614016 model_lib_v2.py:707] Step 79300 per-step time 0.382s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.6801853,
'Loss/BoxClassifierLoss/localization_loss': 0.27682108,
'Loss/RPNLoss/localization_loss': 0.053858586,
'Loss/RPNLoss/objectness_loss': 0.68688375,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 1.6977488,
'learning_rate': 0.017144732}
I0123 15:49:25.945497 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.6801853,
'Loss/BoxClassifierLoss/localization_loss': 0.27682108,
'Loss/RPNLoss/localization_loss': 0.053858586,
'Loss/RPNLoss/objectness_loss': 0.68688375,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 1.6977488,
'learning_rate': 0.017144732}
INFO:tensorflow:Step 79400 per-step time 0.381s
I0123 15:50:04.008481 140287364614016 model_lib_v2.py:707] Step 79400 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0008747508,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 2.3517666,
'Loss/RPNLoss/objectness_loss': 0.59248525,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 2.9451265,
'learning_rate': 0.016985593}
I0123 15:50:04.008798 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0008747508,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 2.3517666,
'Loss/RPNLoss/objectness_loss': 0.59248525,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 2.9451265,
'learning_rate': 0.016985593}
INFO:tensorflow:Step 79500 per-step time 0.379s
I0123 15:50:41.932635 140287364614016 model_lib_v2.py:707] Step 79500 per-step time 0.379s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00048257742,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.84862,
'Loss/RPNLoss/objectness_loss': 0.1088061,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 0.9579087,
'learning_rate': 0.01682712}
I0123 15:50:41.932975 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00048257742,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.84862,
'Loss/RPNLoss/objectness_loss': 0.1088061,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 0.9579087,
'learning_rate': 0.01682712}
INFO:tensorflow:Step 79600 per-step time 0.376s
I0123 15:51:19.498036 140287364614016 model_lib_v2.py:707] Step 79600 per-step time 0.376s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.39011973,
'Loss/BoxClassifierLoss/localization_loss': 0.12240758,
'Loss/RPNLoss/localization_loss': 1.0416999,
'Loss/RPNLoss/objectness_loss': 0.62105924,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 2.1752863,
'learning_rate': 0.016669286}
I0123 15:51:19.498351 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.39011973,
'Loss/BoxClassifierLoss/localization_loss': 0.12240758,
'Loss/RPNLoss/localization_loss': 1.0416999,
'Loss/RPNLoss/objectness_loss': 0.62105924,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 2.1752863,
'learning_rate': 0.016669286}
INFO:tensorflow:Step 79700 per-step time 0.382s
I0123 15:51:57.683816 140287364614016 model_lib_v2.py:707] Step 79700 per-step time 0.382s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.18671319,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.6334281,
'Loss/RPNLoss/objectness_loss': 0.69709295,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 1.5172342,
'learning_rate': 0.016512126}
I0123 15:51:57.684128 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.18671319,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.6334281,
'Loss/RPNLoss/objectness_loss': 0.69709295,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 1.5172342,
'learning_rate': 0.016512126}
INFO:tensorflow:Step 79800 per-step time 0.386s
I0123 15:52:36.293656 140287364614016 model_lib_v2.py:707] Step 79800 per-step time 0.386s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.0334463,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 54.779125,
'Loss/RPNLoss/objectness_loss': 1.5619979,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 56.37457,
'learning_rate': 0.016355604}
I0123 15:52:36.293973 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.0334463,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 54.779125,
'Loss/RPNLoss/objectness_loss': 1.5619979,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 56.37457,
'learning_rate': 0.016355604}
INFO:tensorflow:Step 79900 per-step time 0.384s
I0123 15:53:14.669533 140287364614016 model_lib_v2.py:707] Step 79900 per-step time 0.384s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.009759242,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 1.9375439,
'Loss/RPNLoss/objectness_loss': 1.6338415,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 3.5811446,
'learning_rate': 0.01619976}
I0123 15:53:14.669855 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.009759242,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 1.9375439,
'Loss/RPNLoss/objectness_loss': 1.6338415,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 3.5811446,
'learning_rate': 0.01619976}
INFO:tensorflow:Step 80000 per-step time 0.381s
I0123 15:53:52.814599 140287364614016 model_lib_v2.py:707] Step 80000 per-step time 0.381s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.1444095,
'Loss/BoxClassifierLoss/localization_loss': 0.88752365,
'Loss/RPNLoss/localization_loss': 82.020805,
'Loss/RPNLoss/objectness_loss': 1.8583052,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 85.91104,
'learning_rate': 0.016044574}
I0123 15:53:52.814925 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.1444095,
'Loss/BoxClassifierLoss/localization_loss': 0.88752365,
'Loss/RPNLoss/localization_loss': 82.020805,
'Loss/RPNLoss/objectness_loss': 1.8583052,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 85.91104,
'learning_rate': 0.016044574}
INFO:tensorflow:Step 80100 per-step time 0.411s
I0123 15:54:33.915232 140287364614016 model_lib_v2.py:707] Step 80100 per-step time 0.411s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.0009408,
'Loss/BoxClassifierLoss/localization_loss': 0.523782,
'Loss/RPNLoss/localization_loss': 0.31254238,
'Loss/RPNLoss/objectness_loss': 0.5868543,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 2.4241195,
'learning_rate': 0.015890054}
I0123 15:54:33.915529 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.0009408,
'Loss/BoxClassifierLoss/localization_loss': 0.523782,
'Loss/RPNLoss/localization_loss': 0.31254238,
'Loss/RPNLoss/objectness_loss': 0.5868543,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 2.4241195,
'learning_rate': 0.015890054}
INFO:tensorflow:Step 80200 per-step time 0.385s
I0123 15:55:12.369825 140287364614016 model_lib_v2.py:707] Step 80200 per-step time 0.385s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.8549322,
'Loss/BoxClassifierLoss/localization_loss': 1.5488619,
'Loss/RPNLoss/localization_loss': 26.58376,
'Loss/RPNLoss/objectness_loss': 0.4488349,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 29.436388,
'learning_rate': 0.015736198}
I0123 15:55:12.370136 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.8549322,
'Loss/BoxClassifierLoss/localization_loss': 1.5488619,
'Loss/RPNLoss/localization_loss': 26.58376,
'Loss/RPNLoss/objectness_loss': 0.4488349,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 29.436388,
'learning_rate': 0.015736198}
INFO:tensorflow:Step 80300 per-step time 0.383s
I0123 15:55:50.654390 140287364614016 model_lib_v2.py:707] Step 80300 per-step time 0.383s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.1576238,
'Loss/BoxClassifierLoss/localization_loss': 0.6040846,
'Loss/RPNLoss/localization_loss': 63.860676,
'Loss/RPNLoss/objectness_loss': 1.0205679,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 66.64295,
'learning_rate': 0.015583005}
I0123 15:55:50.654678 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.1576238,
'Loss/BoxClassifierLoss/localization_loss': 0.6040846,
'Loss/RPNLoss/localization_loss': 63.860676,
'Loss/RPNLoss/objectness_loss': 1.0205679,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 66.64295,
'learning_rate': 0.015583005}
INFO:tensorflow:Step 80400 per-step time 0.378s
I0123 15:56:28.493222 140287364614016 model_lib_v2.py:707] Step 80400 per-step time 0.378s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.5179384,
'Loss/BoxClassifierLoss/localization_loss': 1.102843,
'Loss/RPNLoss/localization_loss': 0.39166912,
'Loss/RPNLoss/objectness_loss': 0.27415758,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 3.286608,
'learning_rate': 0.015430493}
I0123 15:56:28.493552 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.5179384,
'Loss/BoxClassifierLoss/localization_loss': 1.102843,
'Loss/RPNLoss/localization_loss': 0.39166912,
'Loss/RPNLoss/objectness_loss': 0.27415758,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 3.286608,
'learning_rate': 0.015430493}
INFO:tensorflow:Step 80500 per-step time 0.386s
I0123 15:57:07.122789 140287364614016 model_lib_v2.py:707] Step 80500 per-step time 0.386s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.423513,
'Loss/BoxClassifierLoss/localization_loss': 0.6655255,
'Loss/RPNLoss/localization_loss': 53.97462,
'Loss/RPNLoss/objectness_loss': 0.85856605,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 56.922226,
'learning_rate': 0.015278635}
I0123 15:57:07.123116 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.423513,
'Loss/BoxClassifierLoss/localization_loss': 0.6655255,
'Loss/RPNLoss/localization_loss': 53.97462,
'Loss/RPNLoss/objectness_loss': 0.85856605,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 56.922226,
'learning_rate': 0.015278635}
INFO:tensorflow:Step 80600 per-step time 0.382s
I0123 15:57:45.293949 140287364614016 model_lib_v2.py:707] Step 80600 per-step time 0.382s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 1.7237444,
'Loss/BoxClassifierLoss/localization_loss': 0.8998036,
'Loss/RPNLoss/localization_loss': 29.695911,
'Loss/RPNLoss/objectness_loss': 0.5629089,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 32.88237,
'learning_rate': 0.015127453}
I0123 15:57:45.294242 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 1.7237444,
'Loss/BoxClassifierLoss/localization_loss': 0.8998036,
'Loss/RPNLoss/localization_loss': 29.695911,
'Loss/RPNLoss/objectness_loss': 0.5629089,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 32.88237,
'learning_rate': 0.015127453}
INFO:tensorflow:Step 80700 per-step time 0.383s
I0123 15:58:23.558974 140287364614016 model_lib_v2.py:707] Step 80700 per-step time 0.383s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.001082171,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 52.320652,
'Loss/RPNLoss/objectness_loss': 0.43119323,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 52.752926,
'learning_rate': 0.014976959}
I0123 15:58:23.559369 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.001082171,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 52.320652,
'Loss/RPNLoss/objectness_loss': 0.43119323,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 52.752926,
'learning_rate': 0.014976959}
INFO:tensorflow:Step 80800 per-step time 0.386s
I0123 15:59:02.129001 140287364614016 model_lib_v2.py:707] Step 80800 per-step time 0.386s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.019703379,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 31.327469,
'Loss/RPNLoss/objectness_loss': 1.307095,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 32.654266,
'learning_rate': 0.014827128}
I0123 15:59:02.129316 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.019703379,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 31.327469,
'Loss/RPNLoss/objectness_loss': 1.307095,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 32.654266,
'learning_rate': 0.014827128}
INFO:tensorflow:Step 80900 per-step time 0.396s
I0123 15:59:41.755171 140287364614016 model_lib_v2.py:707] Step 80900 per-step time 0.396s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.5337414,
'Loss/BoxClassifierLoss/localization_loss': 0.1393968,
'Loss/RPNLoss/localization_loss': 24.967594,
'Loss/RPNLoss/objectness_loss': 0.33450016,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 25.975233,
'learning_rate': 0.014677968}
I0123 15:59:41.755471 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.5337414,
'Loss/BoxClassifierLoss/localization_loss': 0.1393968,
'Loss/RPNLoss/localization_loss': 24.967594,
'Loss/RPNLoss/objectness_loss': 0.33450016,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 25.975233,
'learning_rate': 0.014677968}
INFO:tensorflow:Step 81000 per-step time 0.385s
I0123 16:00:20.294591 140287364614016 model_lib_v2.py:707] Step 81000 per-step time 0.385s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00024240505,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 57.503975,
'Loss/RPNLoss/objectness_loss': 0.20698807,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 57.711205,
'learning_rate': 0.014529499}
I0123 16:00:20.294896 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00024240505,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 57.503975,
'Loss/RPNLoss/objectness_loss': 0.20698807,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 57.711205,
'learning_rate': 0.014529499}
INFO:tensorflow:Step 81100 per-step time 0.418s
I0123 16:01:02.056276 140287364614016 model_lib_v2.py:707] Step 81100 per-step time 0.418s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.11626255,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 6.1249623,
'Loss/RPNLoss/objectness_loss': 2.226023,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 8.467248,
'learning_rate': 0.014381699}
I0123 16:01:02.056622 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.11626255,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 6.1249623,
'Loss/RPNLoss/objectness_loss': 2.226023,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 8.467248,
'learning_rate': 0.014381699}
INFO:tensorflow:Step 81200 per-step time 0.385s
I0123 16:01:40.548032 140287364614016 model_lib_v2.py:707] Step 81200 per-step time 0.385s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.008738259,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 36.51449,
'Loss/RPNLoss/objectness_loss': 0.5205713,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 37.043797,
'learning_rate': 0.014234595}
I0123 16:01:40.548349 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.008738259,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 36.51449,
'Loss/RPNLoss/objectness_loss': 0.5205713,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 37.043797,
'learning_rate': 0.014234595}
INFO:tensorflow:Step 81300 per-step time 0.385s
I0123 16:02:19.061013 140287364614016 model_lib_v2.py:707] Step 81300 per-step time 0.385s
INFO:tensorflow:{'Loss/BoxClassifierLoss/classification_loss': 0.00025892313,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.42648917,
'Loss/RPNLoss/objectness_loss': 0.54572624,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 0.97247434,
'learning_rate': 0.014088149}
I0123 16:02:19.061323 140287364614016 model_lib_v2.py:708] {'Loss/BoxClassifierLoss/classification_loss': 0.00025892313,
'Loss/BoxClassifierLoss/localization_loss': 0.0,
'Loss/RPNLoss/localization_loss': 0.42648917,
'Loss/RPNLoss/objectness_loss': 0.54572624,
'Loss/regularization_loss': 0.0,
'Loss/total_loss': 0.97247434,
'learning_rate': 0.014088149}
^C
!python model_main_tf2.py --model_dir=models/my_rcnn_imagenet --pipeline_config_path=models/my_rcnn_imagenet/pipeline.config --checkpoint_dir=models/my_rcnn_imagenet
WARNING:tensorflow:Forced number of epochs for all eval validations to be 1.
W0124 14:30:30.150501 140299072845696 model_lib_v2.py:1090] Forced number of epochs for all eval validations to be 1.
INFO:tensorflow:Maybe overwriting sample_1_of_n_eval_examples: None
I0124 14:30:30.150705 140299072845696 config_util.py:552] Maybe overwriting sample_1_of_n_eval_examples: None
INFO:tensorflow:Maybe overwriting use_bfloat16: False
I0124 14:30:30.150771 140299072845696 config_util.py:552] Maybe overwriting use_bfloat16: False
INFO:tensorflow:Maybe overwriting eval_num_epochs: 1
I0124 14:30:30.150831 140299072845696 config_util.py:552] Maybe overwriting eval_num_epochs: 1
WARNING:tensorflow:Expected number of evaluation epochs is 1, but instead encountered `eval_on_train_input_config.num_epochs` = 0. Overwriting `num_epochs` to 1.
W0124 14:30:30.150916 140299072845696 model_lib_v2.py:1111] Expected number of evaluation epochs is 1, but instead encountered `eval_on_train_input_config.num_epochs` = 0. Overwriting `num_epochs` to 1.
2022-01-24 14:30:30.894094: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
INFO:tensorflow:Reading unweighted datasets: ['./annotations/test.record']
I0124 14:30:30.957651 140299072845696 dataset_builder.py:163] Reading unweighted datasets: ['./annotations/test.record']
INFO:tensorflow:Reading record datasets for input file: ['./annotations/test.record']
I0124 14:30:30.957953 140299072845696 dataset_builder.py:80] Reading record datasets for input file: ['./annotations/test.record']
INFO:tensorflow:Number of filenames to read: 1
I0124 14:30:30.958027 140299072845696 dataset_builder.py:81] Number of filenames to read: 1
WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.
W0124 14:30:30.958081 140299072845696 dataset_builder.py:88] num_readers has been reduced to 1 to match input file shards.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:105: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.
W0124 14:30:30.960322 140299072845696 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:105: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:237: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.map()
W0124 14:30:30.980329 140299072845696 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:237: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.map()
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.
W0124 14:30:35.073913 140299072845696 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
W0124 14:30:36.242375 140299072845696 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
INFO:tensorflow:Waiting for new checkpoint at models/my_rcnn_imagenet
I0124 14:30:38.818378 140299072845696 checkpoint_utils.py:140] Waiting for new checkpoint at models/my_rcnn_imagenet
INFO:tensorflow:Found new checkpoint at models/my_rcnn_imagenet/ckpt-82
I0124 14:30:38.821352 140299072845696 checkpoint_utils.py:149] Found new checkpoint at models/my_rcnn_imagenet/ckpt-82
/usr/local/lib/python3.7/dist-packages/keras/backend.py:414: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.
warnings.warn('`tf.keras.backend.set_learning_phase` is deprecated and '
INFO:tensorflow:depth of additional conv before box predictor: 0
I0124 14:30:53.359992 140299072845696 convolutional_keras_box_predictor.py:154] depth of additional conv before box predictor: 0
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
W0124 14:31:00.592570 140299072845696 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Future major versions of TensorFlow will allow gradients to flow
into the labels input on backprop by default.
See `tf.nn.softmax_cross_entropy_with_logits_v2`.
W0124 14:31:06.957759 140299072845696 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Future major versions of TensorFlow will allow gradients to flow
into the labels input on backprop by default.
See `tf.nn.softmax_cross_entropy_with_logits_v2`.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/eval_util.py:929: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
W0124 14:32:15.174481 140299072845696 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/object_detection/eval_util.py:929: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
INFO:tensorflow:Finished eval step 0
I0124 14:32:15.183495 140299072845696 model_lib_v2.py:966] Finished eval step 0
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.
Instructions for updating:
tf.py_func is deprecated in TF V2. Instead, there are two
options available in V2.
- tf.py_function takes a python function which manipulates tf eager
tensors instead of numpy arrays. It's easy to convert a tf eager tensor to
an ndarray (just call tensor.numpy()) but having access to eager tensors
means `tf.py_function`s can use accelerators such as GPUs as well as
being differentiable using a gradient tape.
- tf.numpy_function maintains the semantics of the deprecated tf.py_func
(it is not differentiable, and manipulates numpy arrays). It drops the
stateful argument making all functions stateful.
W0124 14:32:15.350790 140299072845696 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.
Instructions for updating:
tf.py_func is deprecated in TF V2. Instead, there are two
options available in V2.
- tf.py_function takes a python function which manipulates tf eager
tensors instead of numpy arrays. It's easy to convert a tf eager tensor to
an ndarray (just call tensor.numpy()) but having access to eager tensors
means `tf.py_function`s can use accelerators such as GPUs as well as
being differentiable using a gradient tape.
- tf.numpy_function maintains the semantics of the deprecated tf.py_func
(it is not differentiable, and manipulates numpy arrays). It drops the
stateful argument making all functions stateful.
INFO:tensorflow:Finished eval step 100
I0124 14:33:26.618737 140299072845696 model_lib_v2.py:966] Finished eval step 100
INFO:tensorflow:Finished eval step 200
I0124 14:34:34.286096 140299072845696 model_lib_v2.py:966] Finished eval step 200
INFO:tensorflow:Finished eval step 300
I0124 14:35:42.027490 140299072845696 model_lib_v2.py:966] Finished eval step 300
INFO:tensorflow:Finished eval step 400
I0124 14:36:49.769926 140299072845696 model_lib_v2.py:966] Finished eval step 400
INFO:tensorflow:Finished eval step 500
I0124 14:37:57.465292 140299072845696 model_lib_v2.py:966] Finished eval step 500
INFO:tensorflow:Finished eval step 600
I0124 14:39:05.096886 140299072845696 model_lib_v2.py:966] Finished eval step 600
INFO:tensorflow:Finished eval step 700
I0124 14:40:13.097734 140299072845696 model_lib_v2.py:966] Finished eval step 700
INFO:tensorflow:Finished eval step 800
I0124 14:41:20.774344 140299072845696 model_lib_v2.py:966] Finished eval step 800
INFO:tensorflow:Finished eval step 900
I0124 14:42:28.617234 140299072845696 model_lib_v2.py:966] Finished eval step 900
INFO:tensorflow:Finished eval step 1000
I0124 14:43:36.402159 140299072845696 model_lib_v2.py:966] Finished eval step 1000
INFO:tensorflow:Finished eval step 1100
I0124 14:44:43.999557 140299072845696 model_lib_v2.py:966] Finished eval step 1100
INFO:tensorflow:Finished eval step 1200
I0124 14:45:51.635493 140299072845696 model_lib_v2.py:966] Finished eval step 1200
INFO:tensorflow:Finished eval step 1300
I0124 14:46:59.413781 140299072845696 model_lib_v2.py:966] Finished eval step 1300
INFO:tensorflow:Finished eval step 1400
I0124 14:48:07.252988 140299072845696 model_lib_v2.py:966] Finished eval step 1400
INFO:tensorflow:Finished eval step 1500
I0124 14:49:14.680690 140299072845696 model_lib_v2.py:966] Finished eval step 1500
INFO:tensorflow:Finished eval step 1600
I0124 14:50:22.385610 140299072845696 model_lib_v2.py:966] Finished eval step 1600
INFO:tensorflow:Performing evaluation on 1629 images.
I0124 14:50:41.293404 140299072845696 coco_evaluation.py:293] Performing evaluation on 1629 images.
creating index...
index created!
INFO:tensorflow:Loading and preparing annotation results...
I0124 14:50:41.296270 140299072845696 coco_tools.py:116] Loading and preparing annotation results...
INFO:tensorflow:DONE (t=0.00s)
I0124 14:50:41.297290 140299072845696 coco_tools.py:138] DONE (t=0.00s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=3.93s).
Accumulating evaluation results...
DONE (t=0.91s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
INFO:tensorflow:Eval metrics at step 81000
I0124 14:50:46.247649 140299072845696 model_lib_v2.py:1015] Eval metrics at step 81000
INFO:tensorflow: + DetectionBoxes_Precision/mAP: 0.000000
I0124 14:50:46.262341 140299072845696 model_lib_v2.py:1018] + DetectionBoxes_Precision/mAP: 0.000000
INFO:tensorflow: + DetectionBoxes_Precision/mAP@.50IOU: 0.000000
I0124 14:50:46.263627 140299072845696 model_lib_v2.py:1018] + DetectionBoxes_Precision/mAP@.50IOU: 0.000000
INFO:tensorflow: + DetectionBoxes_Precision/mAP@.75IOU: 0.000000
I0124 14:50:46.264754 140299072845696 model_lib_v2.py:1018] + DetectionBoxes_Precision/mAP@.75IOU: 0.000000
INFO:tensorflow: + DetectionBoxes_Precision/mAP (small): -1.000000
I0124 14:50:46.265940 140299072845696 model_lib_v2.py:1018] + DetectionBoxes_Precision/mAP (small): -1.000000
INFO:tensorflow: + DetectionBoxes_Precision/mAP (medium): 0.000000
I0124 14:50:46.266924 140299072845696 model_lib_v2.py:1018] + DetectionBoxes_Precision/mAP (medium): 0.000000
INFO:tensorflow: + DetectionBoxes_Precision/mAP (large): 0.000000
I0124 14:50:46.267962 140299072845696 model_lib_v2.py:1018] + DetectionBoxes_Precision/mAP (large): 0.000000
INFO:tensorflow: + DetectionBoxes_Recall/AR@1: 0.000000
I0124 14:50:46.269064 140299072845696 model_lib_v2.py:1018] + DetectionBoxes_Recall/AR@1: 0.000000
INFO:tensorflow: + DetectionBoxes_Recall/AR@10: 0.000000
I0124 14:50:46.270041 140299072845696 model_lib_v2.py:1018] + DetectionBoxes_Recall/AR@10: 0.000000
INFO:tensorflow: + DetectionBoxes_Recall/AR@100: 0.000000
I0124 14:50:46.271005 140299072845696 model_lib_v2.py:1018] + DetectionBoxes_Recall/AR@100: 0.000000
INFO:tensorflow: + DetectionBoxes_Recall/AR@100 (small): -1.000000
I0124 14:50:46.271989 140299072845696 model_lib_v2.py:1018] + DetectionBoxes_Recall/AR@100 (small): -1.000000
INFO:tensorflow: + DetectionBoxes_Recall/AR@100 (medium): 0.000000
I0124 14:50:46.273025 140299072845696 model_lib_v2.py:1018] + DetectionBoxes_Recall/AR@100 (medium): 0.000000
INFO:tensorflow: + DetectionBoxes_Recall/AR@100 (large): 0.000000
I0124 14:50:46.274218 140299072845696 model_lib_v2.py:1018] + DetectionBoxes_Recall/AR@100 (large): 0.000000
INFO:tensorflow: + Loss/RPNLoss/localization_loss: 39.191086
I0124 14:50:46.275136 140299072845696 model_lib_v2.py:1018] + Loss/RPNLoss/localization_loss: 39.191086
INFO:tensorflow: + Loss/RPNLoss/objectness_loss: 0.310603
I0124 14:50:46.276059 140299072845696 model_lib_v2.py:1018] + Loss/RPNLoss/objectness_loss: 0.310603
INFO:tensorflow: + Loss/BoxClassifierLoss/localization_loss: 0.000000
I0124 14:50:46.276993 140299072845696 model_lib_v2.py:1018] + Loss/BoxClassifierLoss/localization_loss: 0.000000
INFO:tensorflow: + Loss/BoxClassifierLoss/classification_loss: 0.000000
I0124 14:50:46.277999 140299072845696 model_lib_v2.py:1018] + Loss/BoxClassifierLoss/classification_loss: 0.000000
INFO:tensorflow: + Loss/regularization_loss: 0.000000
I0124 14:50:46.279058 140299072845696 model_lib_v2.py:1018] + Loss/regularization_loss: 0.000000
INFO:tensorflow: + Loss/total_loss: 39.501694
I0124 14:50:46.280007 140299072845696 model_lib_v2.py:1018] + Loss/total_loss: 39.501694
INFO:tensorflow:Waiting for new checkpoint at models/my_rcnn_imagenet
I0124 14:50:47.107184 140299072845696 checkpoint_utils.py:140] Waiting for new checkpoint at models/my_rcnn_imagenet
!python ./exporter_main_v2.py --input_type image_tensor --pipeline_config_path ./models/my_rcnn_imagenet/pipeline.config --trained_checkpoint_dir ./models/my_rcnn_imagenet/ --output_directory ./exported-models/my_model_rcnn
2022-01-24 15:34:34.650881: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0. WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:464: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version. Instructions for updating: back_prop=False is deprecated. Consider using tf.stop_gradient instead. Instead of: results = tf.map_fn(fn, elems, back_prop=False) Use: results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems)) W0124 15:34:36.505764 140620629907328 deprecation.py:619] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:464: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version. Instructions for updating: back_prop=False is deprecated. Consider using tf.stop_gradient instead. Instead of: results = tf.map_fn(fn, elems, back_prop=False) Use: results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems)) INFO:tensorflow:depth of additional conv before box predictor: 0 I0124 15:34:52.334997 140620629907328 convolutional_keras_box_predictor.py:154] depth of additional conv before box predictor: 0 WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Use ref() instead. W0124 15:35:01.644590 140620629907328 deprecation.py:347] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:465: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Use ref() instead. WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.meta_architectures.faster_rcnn_meta_arch.FasterRCNNMetaArch object at 0x7fe41171d3d0>, because it is not built. W0124 15:36:15.178672 140620629907328 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.meta_architectures.faster_rcnn_meta_arch.FasterRCNNMetaArch object at 0x7fe41171d3d0>, because it is not built. 2022-01-24 15:36:46.982829: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them. W0124 15:38:08.656437 140620629907328 save.py:268] Found untraced functions such as FirstStageBoxPredictor_layer_call_fn, FirstStageBoxPredictor_layer_call_and_return_conditional_losses, mask_rcnn_keras_box_predictor_layer_call_fn, mask_rcnn_keras_box_predictor_layer_call_and_return_conditional_losses, FirstStageBoxPredictor_layer_call_fn while saving (showing 5 of 70). These functions will not be directly callable after loading. INFO:tensorflow:Assets written to: ./exported-models/my_model_rcnn/saved_model/assets I0124 15:38:31.600029 140620629907328 builder_impl.py:784] Assets written to: ./exported-models/my_model_rcnn/saved_model/assets INFO:tensorflow:Writing pipeline config file to ./exported-models/my_model_rcnn/pipeline.config I0124 15:38:33.973511 140620629907328 config_util.py:254] Writing pipeline config file to ./exported-models/my_model_rcnn/pipeline.config
yolo_path = os.path.join(capstone_path,"tensorflow/workspace/training-yolo")
os.chdir(yolo_path)
%%capture
!git clone https://github.com/ultralytics/yolov5.git
%cd /yolov5
!pip install -r requirements.txt
%cd ..
/content/drive/My Drive/capstone/tensorflow/workspace/training-yolo
%cd yolov5
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/yolov5
cd training-yolo
[Errno 2] No such file or directory: 'training-yolo' /content/drive/My Drive/capstone/tensorflow/workspace/training-yolo
ls
car_data_yolo/ preprocess-yolo.py val_csv.csv 'Car names and make.csv' train_csv.csv yolov5/
os.chdir(capstone_path)
!ls
Annotations Dataset resnet_final_weights.h5 arun_notebook df_tr.csv resnet_weights.hdf5 aug_images_resize df_ts.csv tensorflow augmented_images.csv labels.csv test.csv 'Car Images' losses.png tmp compressed_images model_plot.png train.csv compressed_test_images resnet152_weights_tf.h5 train_csv.csv compressed_val_images resnet_best_weights.hdf5 val_csv.csv dataframes resnet_final.h5 'yolo best'
#!cp -r compressed_images yolo-path
#!cp -r compressed_val_images yolo-path
#!cp -r compressed_test_images yolo-path
dp = '/content/drive/My Drive/capstone/tensorflow/workspace/training-yolo'
dp_train = os.path.join(dp,'compressed_images')
dp_test = os.path.join(dp,'compressed_test_images')
dp_val = os.path.join(dp,'compressed_val_images')
os.chdir(dp)
out_dp = '/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo'
df_train
| filename | width | height | class | xmin | xmax | ymin | ymax | class_name | filepath | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 06890.jpg | 1600 | 1200 | 108 | 196.0 | 1372.0 | 43.0 | 1156.0 | Ford Freestar Minivan 2007 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 1 | 03838.jpg | 259 | 194 | 14 | 13.0 | 251.0 | 30.0 | 156.0 | Audi TTS Coupe 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 2 | 03741.jpg | 300 | 180 | 100 | 21.0 | 263.0 | 4.0 | 150.0 | FIAT 500 Convertible 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 3 | 05159.jpg | 600 | 450 | 114 | 9.0 | 600.0 | 66.0 | 373.0 | Ford F-150 Regular Cab 2007 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 4 | 03349.jpg | 650 | 460 | 25 | 26.0 | 632.0 | 104.0 | 392.0 | Audi TT RS Coupe 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 6510 | 06603.jpg | 628 | 405 | 7 | 39.0 | 595.0 | 127.0 | 360.0 | Acura ZDX Hatchback 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 6511 | 03612.jpg | 438 | 329 | 69 | 80.0 | 350.0 | 40.0 | 283.0 | Chevrolet Silverado 2500HD Regular Cab 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 6512 | 00754.jpg | 625 | 468 | 95 | 60.0 | 578.0 | 2.0 | 440.0 | Dodge Durango SUV 2007 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 6513 | 08125.jpg | 318 | 180 | 55 | 24.0 | 225.0 | 85.0 | 159.0 | Chevrolet Corvette Convertible 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 6514 | 07391.jpg | 320 | 240 | 146 | 21.0 | 319.0 | 36.0 | 198.0 | Jeep Wrangler SUV 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
6515 rows × 10 columns
df_val
| filename | width | height | class | xmin | xmax | ymin | ymax | class_name | filepath | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 04506.jpg | 1600 | 1200 | 63 | 478.0 | 1534.0 | 286.0 | 979.0 | Chevrolet Sonic Sedan 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 1 | 04600.jpg | 259 | 194 | 190 | 17.0 | 241.0 | 47.0 | 158.0 | Volkswagen Golf Hatchback 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 2 | 04462.jpg | 640 | 480 | 85 | 9.0 | 617.0 | 26.0 | 445.0 | Dodge Caravan Minivan 1997 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 3 | 02906.jpg | 640 | 480 | 49 | 54.0 | 603.0 | 77.0 | 385.0 | Buick Verano Sedan 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 4 | 05184.jpg | 630 | 418 | 83 | 24.0 | 607.0 | 144.0 | 398.0 | Dodge Caliber Wagon 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1624 | 02584.jpg | 480 | 360 | 12 | 33.0 | 458.0 | 48.0 | 289.0 | Audi RS 4 Convertible 2008 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 1625 | 01438.jpg | 800 | 600 | 33 | 16.0 | 768.0 | 132.0 | 574.0 | BMW X6 SUV 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 1626 | 03655.jpg | 1024 | 768 | 43 | 72.0 | 992.0 | 101.0 | 648.0 | Bentley Continental GT Coupe 2007 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 1627 | 06440.jpg | 485 | 323 | 181 | 18.0 | 468.0 | 135.0 | 314.0 | Suzuki Aerio Sedan 2007 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 1628 | 02737.jpg | 600 | 450 | 196 | 50.0 | 558.0 | 72.0 | 419.0 | smart fortwo Convertible 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
1629 rows × 10 columns
df_test_ann
| filename | width | height | class | xmin | xmax | ymin | ymax | class_name | filepath | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 00001.jpg | 276 | 182 | 181 | 30.0 | 246.0 | 52.0 | 147.0 | Suzuki Aerio Sedan 2007 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 1 | 00002.jpg | 640 | 360 | 103 | 100.0 | 576.0 | 19.0 | 203.0 | Ferrari 458 Italia Convertible 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 2 | 00003.jpg | 1024 | 741 | 145 | 51.0 | 968.0 | 105.0 | 659.0 | Jeep Patriot SUV 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 3 | 00004.jpg | 640 | 480 | 187 | 67.0 | 581.0 | 84.0 | 407.0 | Toyota Camry Sedan 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 4 | 00005.jpg | 600 | 373 | 185 | 140.0 | 593.0 | 151.0 | 339.0 | Tesla Model S Sedan 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 8036 | 08037.jpg | 1200 | 800 | 63 | 49.0 | 1169.0 | 57.0 | 669.0 | Chevrolet Sonic Sedan 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 8037 | 08038.jpg | 640 | 480 | 16 | 23.0 | 640.0 | 18.0 | 459.0 | Audi V8 Sedan 1994 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 8038 | 08039.jpg | 635 | 303 | 17 | 33.0 | 602.0 | 27.0 | 252.0 | Audi 100 Sedan 1994 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 8039 | 08040.jpg | 640 | 427 | 38 | 33.0 | 521.0 | 142.0 | 376.0 | BMW Z4 Convertible 2012 | /content/drive/My Drive/capstone/Dataset/Car I... |
| 8040 | 08041.jpg | 640 | 426 | 32 | 77.0 | 506.0 | 73.0 | 380.0 | BMW X5 SUV 2007 | /content/drive/My Drive/capstone/Dataset/Car I... |
8041 rows × 10 columns
df_cars_name = pd.read_csv(os.path.join(dp,'Car names and make.csv'),names=['Car Name'])
df_cars_name
| Car Name | |
|---|---|
| 0 | AM General Hummer SUV 2000 |
| 1 | Acura RL Sedan 2012 |
| 2 | Acura TL Sedan 2012 |
| 3 | Acura TL Type-S 2008 |
| 4 | Acura TSX Sedan 2012 |
| ... | ... |
| 191 | Volkswagen Beetle Hatchback 2012 |
| 192 | Volvo C30 Hatchback 2012 |
| 193 | Volvo 240 Sedan 1993 |
| 194 | Volvo XC90 SUV 2007 |
| 195 | smart fortwo Convertible 2012 |
196 rows × 1 columns
!ls
car_data_yolo preprocess-yolo.py val_csv.csv 'Car names and make.csv' train_csv.csv yolov5
count = 0
for root, dirs, files in os.walk(dp_train):
print(root,dirs,files)
for file in files:
#print(file)
count = count + 1
path_file = os.path.join(root,file)
shutil.copy2(path_file,os.path.join(out_dp + '/train/images'))
print(count)
0
for root, dirs, files in os.walk(dp_test):
for file in files:
path_file = os.path.join(root,file)
shutil.copy2(path_file,out_dp + '/test/images')
count = 0
for root, dirs, files in os.walk(dp_val):
print(root,dirs,files)
for file in files:
count = count + 1
path_file = os.path.join(root,file)
shutil.copy2(path_file,os.path.join(out_dp,'val/images'))
print(count)
0
df_test = df_test_ann.copy()
df_train['filepath'] = df_train['filename'].apply(lambda x:os.path.join(out_dp + '/train/images',x))
df_test['filepath'] = df_test['filename'].apply(lambda x:os.path.join(out_dp + '/test/images',x))
df_val['filepath'] = df_val['filename'].apply(lambda x:os.path.join(out_dp + '/val/images',x))
l = list(df_train['filepath'])
print(l[0])
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/images/06890.jpg
l = list(df_test['filepath'])
print(l[0])
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/test/images/00001.jpg
l = list(df_val['filepath'])
print(l[0])
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/val/images/04506.jpg
df_train['Label_Path'] = df_train['filename'].apply(lambda x:os.path.join(out_dp + '/train/labels',x.replace("jpg", "txt")))
df_test['Label_Path'] = df_test['filename'].apply(lambda x:os.path.join(out_dp + '/test/labels',x.replace("jpg", "txt")))
df_val['Label_Path'] = df_val['filename'].apply(lambda x:os.path.join(out_dp + '/val/labels',x.replace("jpg", "txt")))
l = list(df_val['Label_Path'])
print(l[0])
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/val/labels/04506.txt
l = list(df_test['Label_Path'])
print(l[0])
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/test/labels/00001.txt
l = list(df_train['Label_Path'])
print(l[0])
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/06890.txt
for index, row in df_train.iterrows():
x, y, w, h = YOLO_xywh(row['xmin'],row['ymin'],row['xmax'],row['ymax'],row['width'], row['height'])
image_class_index = int(row['class']) - 1
print(row['Label_Path'])
with open(row['Label_Path'], "w") as text_file:
print(f"{image_class_index} {x} {y} {w} {h}", file=text_file)
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/06890.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03838.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03741.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05159.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03349.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03434.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02692.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02499.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07748.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02472.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/00432.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02995.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/01152.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07215.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03701.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07006.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/01421.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/01781.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/04282.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/06881.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02944.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03646.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/00282.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03692.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05733.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/04385.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/04614.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/06260.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07158.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03211.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03466.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05629.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05598.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/04011.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02895.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/05585.txt 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/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/06508.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/02246.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/06283.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/07140.txt /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels/03329.txt
for index, row in df_val.iterrows():
x, y, w, h = YOLO_xywh(row['xmin'], row['ymin'],row['xmax'],row['ymax'],row['width'], row['height'])
image_class_index = int(row['class']) - 1
print(row['Label_Path'])
with open(row['Label_Path'], "w") as text_file:
print(f"{image_class_index} {x} {y} {w} {h}", file=text_file)
for index, row in df_test.iterrows():
x, y, w, h = YOLO_xywh(row['xmin'],
row['ymin'],
row['xmax'],
row['ymax'],
row['width'],
row['height'])
image_class_index = int(row['class']) - 1
print(row['Label_Path'])
with open(row['Label_Path'], "w") as text_file:
print(f"{image_class_index} {x} {y} {w} {h}", file=text_file)
%cd yolov5
/content/drive/My Drive/capstone/tensorflow/workspace/training-yolo/yolov5
%cd data
/content/drive/My Drive/capstone/tensorflow/workspace/training-yolo/yolov5/data
!touch carData.yaml
%%writefile carData.yaml
train: /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/images
val: /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/val/images
test: /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/test/images
# number of classes
nc: 196
# class names
names: ['AM General Hummer SUV 2000', 'Acura RL Sedan 2012', 'Acura TL Sedan 2012', 'Acura TL Type-S 2008', 'Acura TSX Sedan 2012', 'Acura Integra Type R 2001', 'Acura ZDX Hatchback 2012', 'Aston Martin V8 Vantage Convertible 2012', 'Aston Martin V8 Vantage Coupe 2012', 'Aston Martin Virage Convertible 2012', 'Aston Martin Virage Coupe 2012', 'Audi RS 4 Convertible 2008', 'Audi A5 Coupe 2012', 'Audi TTS Coupe 2012', 'Audi R8 Coupe 2012', 'Audi V8 Sedan 1994', 'Audi 100 Sedan 1994', 'Audi 100 Wagon 1994', 'Audi TT Hatchback 2011', 'Audi S6 Sedan 2011', 'Audi S5 Convertible 2012', 'Audi S5 Coupe 2012', 'Audi S4 Sedan 2012', 'Audi S4 Sedan 2007', 'Audi TT RS Coupe 2012', 'BMW ActiveHybrid 5 Sedan 2012', 'BMW 1 Series Convertible 2012', 'BMW 1 Series Coupe 2012', 'BMW 3 Series Sedan 2012', 'BMW 3 Series Wagon 2012', 'BMW 6 Series Convertible 2007', 'BMW X5 SUV 2007', 'BMW X6 SUV 2012', 'BMW M3 Coupe 2012', 'BMW M5 Sedan 2010', 'BMW M6 Convertible 2010', 'BMW X3 SUV 2012', 'BMW Z4 Convertible 2012', 'Bentley Continental Supersports Conv. Convertible 2012', 'Bentley Arnage Sedan 2009', 'Bentley Mulsanne Sedan 2011', 'Bentley Continental GT Coupe 2012', 'Bentley Continental GT Coupe 2007', 'Bentley Continental Flying Spur Sedan 2007', 'Bugatti Veyron 16.4 Convertible 2009', 'Bugatti Veyron 16.4 Coupe 2009', 'Buick Regal GS 2012', 'Buick Rainier SUV 2007', 'Buick Verano Sedan 2012', 'Buick Enclave SUV 2012', 'Cadillac CTS-V Sedan 2012', 'Cadillac SRX SUV 2012', 'Cadillac Escalade EXT Crew Cab 2007', 'Chevrolet Silverado 1500 Hybrid Crew Cab 2012', 'Chevrolet Corvette Convertible 2012', 'Chevrolet Corvette ZR1 2012', 'Chevrolet Corvette Ron Fellows Edition Z06 2007', 'Chevrolet Traverse SUV 2012', 'Chevrolet Camaro Convertible 2012', 'Chevrolet HHR SS 2010', 'Chevrolet Impala Sedan 2007', 'Chevrolet Tahoe Hybrid SUV 2012', 'Chevrolet Sonic Sedan 2012', 'Chevrolet Express Cargo Van 2007', 'Chevrolet Avalanche Crew Cab 2012', 'Chevrolet Cobalt SS 2010', 'Chevrolet Malibu Hybrid Sedan 2010', 'Chevrolet TrailBlazer SS 2009', 'Chevrolet Silverado 2500HD Regular Cab 2012', 'Chevrolet Silverado 1500 Classic Extended Cab 2007', 'Chevrolet Express Van 2007', 'Chevrolet Monte Carlo Coupe 2007', 'Chevrolet Malibu Sedan 2007', 'Chevrolet Silverado 1500 Extended Cab 2012', 'Chevrolet Silverado 1500 Regular Cab 2012', 'Chrysler Aspen SUV 2009', 'Chrysler Sebring Convertible 2010', 'Chrysler Town and Country Minivan 2012', 'Chrysler 300 SRT-8 2010', 'Chrysler Crossfire Convertible 2008', 'Chrysler PT Cruiser Convertible 2008', 'Daewoo Nubira Wagon 2002', 'Dodge Caliber Wagon 2012', 'Dodge Caliber Wagon 2007', 'Dodge Caravan Minivan 1997', 'Dodge Ram Pickup 3500 Crew Cab 2010', 'Dodge Ram Pickup 3500 Quad Cab 2009', 'Dodge Sprinter Cargo Van 2009', 'Dodge Journey SUV 2012', 'Dodge Dakota Crew Cab 2010', 'Dodge Dakota Club Cab 2007', 'Dodge Magnum Wagon 2008', 'Dodge Challenger SRT8 2011', 'Dodge Durango SUV 2012', 'Dodge Durango SUV 2007', 'Dodge Charger Sedan 2012', 'Dodge Charger SRT-8 2009', 'Eagle Talon Hatchback 1998', 'FIAT 500 Abarth 2012', 'FIAT 500 Convertible 2012', 'Ferrari FF Coupe 2012', 'Ferrari California Convertible 2012', 'Ferrari 458 Italia Convertible 2012', 'Ferrari 458 Italia Coupe 2012', 'Fisker Karma Sedan 2012', 'Ford F-450 Super Duty Crew Cab 2012', 'Ford Mustang Convertible 2007', 'Ford Freestar Minivan 2007', 'Ford Expedition EL SUV 2009', 'Ford Edge SUV 2012', 'Ford Ranger SuperCab 2011', 'Ford GT Coupe 2006', 'Ford F-150 Regular Cab 2012', 'Ford F-150 Regular Cab 2007', 'Ford Focus Sedan 2007', 'Ford E-Series Wagon Van 2012', 'Ford Fiesta Sedan 2012', 'GMC Terrain SUV 2012', 'GMC Savana Van 2012', 'GMC Yukon Hybrid SUV 2012', 'GMC Acadia SUV 2012', 'GMC Canyon Extended Cab 2012', 'Geo Metro Convertible 1993', 'HUMMER H3T Crew Cab 2010', 'HUMMER H2 SUT Crew Cab 2009', 'Honda Odyssey Minivan 2012', 'Honda Odyssey Minivan 2007', 'Honda Accord Coupe 2012', 'Honda Accord Sedan 2012', 'Hyundai Veloster Hatchback 2012', 'Hyundai Santa Fe SUV 2012', 'Hyundai Tucson SUV 2012', 'Hyundai Veracruz SUV 2012', 'Hyundai Sonata Hybrid Sedan 2012', 'Hyundai Elantra Sedan 2007', 'Hyundai Accent Sedan 2012', 'Hyundai Genesis Sedan 2012', 'Hyundai Sonata Sedan 2012', 'Hyundai Elantra Touring Hatchback 2012', 'Hyundai Azera Sedan 2012', 'Infiniti G Coupe IPL 2012', 'Infiniti QX56 SUV 2011', 'Isuzu Ascender SUV 2008', 'Jaguar XK XKR 2012', 'Jeep Patriot SUV 2012', 'Jeep Wrangler SUV 2012', 'Jeep Liberty SUV 2012', 'Jeep Grand Cherokee SUV 2012', 'Jeep Compass SUV 2012', 'Lamborghini Reventon Coupe 2008', 'Lamborghini Aventador Coupe 2012', 'Lamborghini Gallardo LP 570-4 Superleggera 2012', 'Lamborghini Diablo Coupe 2001', 'Land Rover Range Rover SUV 2012', 'Land Rover LR2 SUV 2012', 'Lincoln Town Car Sedan 2011', 'MINI Cooper Roadster Convertible 2012', 'Maybach Landaulet Convertible 2012', 'Mazda Tribute SUV 2011', 'McLaren MP4-12C Coupe 2012', 'Mercedes-Benz 300-Class Convertible 1993', 'Mercedes-Benz C-Class Sedan 2012', 'Mercedes-Benz SL-Class Coupe 2009', 'Mercedes-Benz E-Class Sedan 2012', 'Mercedes-Benz S-Class Sedan 2012', 'Mercedes-Benz Sprinter Van 2012', 'Mitsubishi Lancer Sedan 2012', 'Nissan Leaf Hatchback 2012', 'Nissan NV Passenger Van 2012', 'Nissan Juke Hatchback 2012', 'Nissan 240SX Coupe 1998', 'Plymouth Neon Coupe 1999', 'Porsche Panamera Sedan 2012', 'Ram C/V Cargo Van Minivan 2012', 'Rolls-Royce Phantom Drophead Coupe Convertible 2012', 'Rolls-Royce Ghost Sedan 2012', 'Rolls-Royce Phantom Sedan 2012', 'Scion xD Hatchback 2012', 'Spyker C8 Convertible 2009', 'Spyker C8 Coupe 2009', 'Suzuki Aerio Sedan 2007', 'Suzuki Kizashi Sedan 2012', 'Suzuki SX4 Hatchback 2012', 'Suzuki SX4 Sedan 2012', 'Tesla Model S Sedan 2012', 'Toyota Sequoia SUV 2012', 'Toyota Camry Sedan 2012', 'Toyota Corolla Sedan 2012', 'Toyota 4Runner SUV 2012', 'Volkswagen Golf Hatchback 2012', 'Volkswagen Golf Hatchback 1991', 'Volkswagen Beetle Hatchback 2012', 'Volvo C30 Hatchback 2012', 'Volvo 240 Sedan 1993', 'Volvo XC90 SUV 2007', 'smart fortwo Convertible 2012']
%cat carData.yaml
train: /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/images val: /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/val/images test: /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/test/images # number of classes nc: 196 # class names names: ['AM General Hummer SUV 2000', 'Acura RL Sedan 2012', 'Acura TL Sedan 2012', 'Acura TL Type-S 2008', 'Acura TSX Sedan 2012', 'Acura Integra Type R 2001', 'Acura ZDX Hatchback 2012', 'Aston Martin V8 Vantage Convertible 2012', 'Aston Martin V8 Vantage Coupe 2012', 'Aston Martin Virage Convertible 2012', 'Aston Martin Virage Coupe 2012', 'Audi RS 4 Convertible 2008', 'Audi A5 Coupe 2012', 'Audi TTS Coupe 2012', 'Audi R8 Coupe 2012', 'Audi V8 Sedan 1994', 'Audi 100 Sedan 1994', 'Audi 100 Wagon 1994', 'Audi TT Hatchback 2011', 'Audi S6 Sedan 2011', 'Audi S5 Convertible 2012', 'Audi S5 Coupe 2012', 'Audi S4 Sedan 2012', 'Audi S4 Sedan 2007', 'Audi TT RS Coupe 2012', 'BMW ActiveHybrid 5 Sedan 2012', 'BMW 1 Series Convertible 2012', 'BMW 1 Series Coupe 2012', 'BMW 3 Series Sedan 2012', 'BMW 3 Series Wagon 2012', 'BMW 6 Series Convertible 2007', 'BMW X5 SUV 2007', 'BMW X6 SUV 2012', 'BMW M3 Coupe 2012', 'BMW M5 Sedan 2010', 'BMW M6 Convertible 2010', 'BMW X3 SUV 2012', 'BMW Z4 Convertible 2012', 'Bentley Continental Supersports Conv. Convertible 2012', 'Bentley Arnage Sedan 2009', 'Bentley Mulsanne Sedan 2011', 'Bentley Continental GT Coupe 2012', 'Bentley Continental GT Coupe 2007', 'Bentley Continental Flying Spur Sedan 2007', 'Bugatti Veyron 16.4 Convertible 2009', 'Bugatti Veyron 16.4 Coupe 2009', 'Buick Regal GS 2012', 'Buick Rainier SUV 2007', 'Buick Verano Sedan 2012', 'Buick Enclave SUV 2012', 'Cadillac CTS-V Sedan 2012', 'Cadillac SRX SUV 2012', 'Cadillac Escalade EXT Crew Cab 2007', 'Chevrolet Silverado 1500 Hybrid Crew Cab 2012', 'Chevrolet Corvette Convertible 2012', 'Chevrolet Corvette ZR1 2012', 'Chevrolet Corvette Ron Fellows Edition Z06 2007', 'Chevrolet Traverse SUV 2012', 'Chevrolet Camaro Convertible 2012', 'Chevrolet HHR SS 2010', 'Chevrolet Impala Sedan 2007', 'Chevrolet Tahoe Hybrid SUV 2012', 'Chevrolet Sonic Sedan 2012', 'Chevrolet Express Cargo Van 2007', 'Chevrolet Avalanche Crew Cab 2012', 'Chevrolet Cobalt SS 2010', 'Chevrolet Malibu Hybrid Sedan 2010', 'Chevrolet TrailBlazer SS 2009', 'Chevrolet Silverado 2500HD Regular Cab 2012', 'Chevrolet Silverado 1500 Classic Extended Cab 2007', 'Chevrolet Express Van 2007', 'Chevrolet Monte Carlo Coupe 2007', 'Chevrolet Malibu Sedan 2007', 'Chevrolet Silverado 1500 Extended Cab 2012', 'Chevrolet Silverado 1500 Regular Cab 2012', 'Chrysler Aspen SUV 2009', 'Chrysler Sebring Convertible 2010', 'Chrysler Town and Country Minivan 2012', 'Chrysler 300 SRT-8 2010', 'Chrysler Crossfire Convertible 2008', 'Chrysler PT Cruiser Convertible 2008', 'Daewoo Nubira Wagon 2002', 'Dodge Caliber Wagon 2012', 'Dodge Caliber Wagon 2007', 'Dodge Caravan Minivan 1997', 'Dodge Ram Pickup 3500 Crew Cab 2010', 'Dodge Ram Pickup 3500 Quad Cab 2009', 'Dodge Sprinter Cargo Van 2009', 'Dodge Journey SUV 2012', 'Dodge Dakota Crew Cab 2010', 'Dodge Dakota Club Cab 2007', 'Dodge Magnum Wagon 2008', 'Dodge Challenger SRT8 2011', 'Dodge Durango SUV 2012', 'Dodge Durango SUV 2007', 'Dodge Charger Sedan 2012', 'Dodge Charger SRT-8 2009', 'Eagle Talon Hatchback 1998', 'FIAT 500 Abarth 2012', 'FIAT 500 Convertible 2012', 'Ferrari FF Coupe 2012', 'Ferrari California Convertible 2012', 'Ferrari 458 Italia Convertible 2012', 'Ferrari 458 Italia Coupe 2012', 'Fisker Karma Sedan 2012', 'Ford F-450 Super Duty Crew Cab 2012', 'Ford Mustang Convertible 2007', 'Ford Freestar Minivan 2007', 'Ford Expedition EL SUV 2009', 'Ford Edge SUV 2012', 'Ford Ranger SuperCab 2011', 'Ford GT Coupe 2006', 'Ford F-150 Regular Cab 2012', 'Ford F-150 Regular Cab 2007', 'Ford Focus Sedan 2007', 'Ford E-Series Wagon Van 2012', 'Ford Fiesta Sedan 2012', 'GMC Terrain SUV 2012', 'GMC Savana Van 2012', 'GMC Yukon Hybrid SUV 2012', 'GMC Acadia SUV 2012', 'GMC Canyon Extended Cab 2012', 'Geo Metro Convertible 1993', 'HUMMER H3T Crew Cab 2010', 'HUMMER H2 SUT Crew Cab 2009', 'Honda Odyssey Minivan 2012', 'Honda Odyssey Minivan 2007', 'Honda Accord Coupe 2012', 'Honda Accord Sedan 2012', 'Hyundai Veloster Hatchback 2012', 'Hyundai Santa Fe SUV 2012', 'Hyundai Tucson SUV 2012', 'Hyundai Veracruz SUV 2012', 'Hyundai Sonata Hybrid Sedan 2012', 'Hyundai Elantra Sedan 2007', 'Hyundai Accent Sedan 2012', 'Hyundai Genesis Sedan 2012', 'Hyundai Sonata Sedan 2012', 'Hyundai Elantra Touring Hatchback 2012', 'Hyundai Azera Sedan 2012', 'Infiniti G Coupe IPL 2012', 'Infiniti QX56 SUV 2011', 'Isuzu Ascender SUV 2008', 'Jaguar XK XKR 2012', 'Jeep Patriot SUV 2012', 'Jeep Wrangler SUV 2012', 'Jeep Liberty SUV 2012', 'Jeep Grand Cherokee SUV 2012', 'Jeep Compass SUV 2012', 'Lamborghini Reventon Coupe 2008', 'Lamborghini Aventador Coupe 2012', 'Lamborghini Gallardo LP 570-4 Superleggera 2012', 'Lamborghini Diablo Coupe 2001', 'Land Rover Range Rover SUV 2012', 'Land Rover LR2 SUV 2012', 'Lincoln Town Car Sedan 2011', 'MINI Cooper Roadster Convertible 2012', 'Maybach Landaulet Convertible 2012', 'Mazda Tribute SUV 2011', 'McLaren MP4-12C Coupe 2012', 'Mercedes-Benz 300-Class Convertible 1993', 'Mercedes-Benz C-Class Sedan 2012', 'Mercedes-Benz SL-Class Coupe 2009', 'Mercedes-Benz E-Class Sedan 2012', 'Mercedes-Benz S-Class Sedan 2012', 'Mercedes-Benz Sprinter Van 2012', 'Mitsubishi Lancer Sedan 2012', 'Nissan Leaf Hatchback 2012', 'Nissan NV Passenger Van 2012', 'Nissan Juke Hatchback 2012', 'Nissan 240SX Coupe 1998', 'Plymouth Neon Coupe 1999', 'Porsche Panamera Sedan 2012', 'Ram C/V Cargo Van Minivan 2012', 'Rolls-Royce Phantom Drophead Coupe Convertible 2012', 'Rolls-Royce Ghost Sedan 2012', 'Rolls-Royce Phantom Sedan 2012', 'Scion xD Hatchback 2012', 'Spyker C8 Convertible 2009', 'Spyker C8 Coupe 2009', 'Suzuki Aerio Sedan 2007', 'Suzuki Kizashi Sedan 2012', 'Suzuki SX4 Hatchback 2012', 'Suzuki SX4 Sedan 2012', 'Tesla Model S Sedan 2012', 'Toyota Sequoia SUV 2012', 'Toyota Camry Sedan 2012', 'Toyota Corolla Sedan 2012', 'Toyota 4Runner SUV 2012', 'Volkswagen Golf Hatchback 2012', 'Volkswagen Golf Hatchback 1991', 'Volkswagen Beetle Hatchback 2012', 'Volvo C30 Hatchback 2012', 'Volvo 240 Sedan 1993', 'Volvo XC90 SUV 2007', 'smart fortwo Convertible 2012']
%cd /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/yolov5
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/yolov5
!python train.py --img 224 --cfg yolov5s.yaml --hyp hyp.scratch.yaml --batch 32 --epochs 200 --data carData.yaml --weights yolov5s.pt --workers 16 --name yolo_car_classification
Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf... train: weights=yolov5s.pt, cfg=yolov5s.yaml, data=carData.yaml, hyp=hyp.scratch.yaml, epochs=200, batch_size=32, imgsz=224, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=16, project=runs/train, name=yolo_car_classification, exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest github: Command 'git fetch && git config --get remote.origin.url' timed out after 5 seconds YOLOv5 🚀 v6.0-200-g9708cf5 torch 1.10.0+cu111 CUDA:0 (Tesla P100-PCIE-16GB, 16281MiB) hyperparameters: lr0=0.01, lrf=0.1, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0 Weights & Biases: run 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED) TensorBoard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/ remote: Enumerating objects: 311, done. remote: Counting objects: 100% (130/130), done. remote: Total 311 (delta 130), reused 130 (delta 130), pack-reused 181 Receiving objects: 100% (311/311), 71.26 KiB | 51.00 KiB/s, done. Resolving deltas: 100% (201/201), completed with 43 local objects. Overriding model.yaml nc=80 with nc=196 from n params module arguments 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] 2 -1 1 18816 models.common.C3 [64, 64, 1] 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] 4 -1 2 115712 models.common.C3 [128, 128, 2] 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] 6 -1 3 625152 models.common.C3 [256, 256, 3] 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] 8 -1 1 1182720 models.common.C3 [512, 512, 1] 9 -1 1 656896 models.common.SPPF [512, 512, 5] 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 12 [-1, 6] 1 0 models.common.Concat [1] 13 -1 1 361984 models.common.C3 [512, 256, 1, False] 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 16 [-1, 4] 1 0 models.common.Concat [1] 17 -1 1 90880 models.common.C3 [256, 128, 1, False] 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] 19 [-1, 14] 1 0 models.common.Concat [1] 20 -1 1 296448 models.common.C3 [256, 256, 1, False] 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] 22 [-1, 10] 1 0 models.common.Concat [1] 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] 24 [17, 20, 23] 1 542097 models.yolo.Detect [196, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]] Model Summary: 270 layers, 7548241 parameters, 7548241 gradients Transferred 342/349 items from yolov5s.pt Scaled weight_decay = 0.0005 optimizer: SGD with parameter groups 57 weight (no decay), 60 weight, 60 bias albumentations: version 1.0.3 required by YOLOv5, but version 0.1.12 is currently installed From https://github.com/ultralytics/yolov5 16563ac..7539cd7 master -> origin/master 4c9e629..46a2a70 classifier -> origin/classifier 241cd84..487a014 tests/aws -> origin/tests/aws * [new branch] updates/benchmarks -> origin/updates/benchmarks train: Scanning '/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/labels.cache' images and labels... 6515 found, 0 missing, 1 empty, 1 corrupt: 100% 6515/6515 [00:00<?, ?it/s] train: WARNING: /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/train/images/07389.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1476] val: Scanning '/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/val/labels.cache' images and labels... 1629 found, 0 missing, 0 empty, 0 corrupt: 100% 1629/1629 [00:00<?, ?it/s] Plotting labels to runs/train/yolo_car_classification6/labels.jpg... AutoAnchor: 4.80 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅ Image sizes 224 train, 224 val Using 4 dataloader workers Logging results to runs/train/yolo_car_classification6 Starting training for 200 epochs... Epoch gpu_mem box obj cls labels img_size 0/199 1.11G 0.05393 0.02021 0.1235 46 224: 100% 204/204 [02:41<00:00, 1.26it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [03:48<00:00, 8.78s/it] all 1629 1629 0.00523 0.606 0.0112 0.00769 Epoch gpu_mem box obj cls labels img_size 1/199 1.73G 0.02907 0.01297 0.1209 46 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.75it/s] all 1629 1629 0.00503 0.996 0.0109 0.00893 Epoch gpu_mem box obj cls labels img_size 2/199 1.73G 0.02766 0.011 0.1198 53 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.80it/s] all 1629 1629 0.00508 1 0.0108 0.00914 Epoch gpu_mem box obj cls labels img_size 3/199 1.73G 0.02596 0.01018 0.1194 49 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.83it/s] all 1629 1629 0.00546 0.346 0.0102 0.00881 Epoch gpu_mem box obj cls labels img_size 4/199 1.73G 0.02249 0.009612 0.1191 46 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.77it/s] all 1629 1629 0.00548 0.412 0.0114 0.0094 Epoch gpu_mem box obj cls labels img_size 5/199 1.73G 0.01943 0.009082 0.1188 48 224: 100% 204/204 [02:34<00:00, 1.32it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00, 1.90it/s] all 1629 1629 0.00632 0.393 0.0128 0.0114 Epoch gpu_mem box obj cls labels img_size 6/199 1.73G 0.01775 0.008705 0.1186 52 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00, 1.73it/s] all 1629 1629 0.00615 0.539 0.0123 0.011 Epoch gpu_mem box obj cls labels img_size 7/199 1.73G 0.01672 0.008265 0.1184 49 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00, 1.72it/s] all 1629 1629 0.0105 0.545 0.012 0.0107 Epoch gpu_mem box obj cls labels img_size 8/199 1.73G 0.01618 0.008115 0.1183 49 224: 100% 204/204 [02:32<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00, 1.70it/s] all 1629 1629 0.00551 0.581 0.0117 0.0105 Epoch gpu_mem box obj cls labels img_size 9/199 1.73G 0.0157 0.008062 0.1182 54 224: 100% 204/204 [02:34<00:00, 1.32it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.79it/s] all 1629 1629 0.00617 0.627 0.0133 0.0119 Epoch gpu_mem box obj cls labels img_size 10/199 1.73G 0.01529 0.007937 0.1181 55 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00, 1.66it/s] all 1629 1629 0.0327 0.184 0.017 0.0149 Epoch gpu_mem box obj cls labels img_size 11/199 1.73G 0.01509 0.007889 0.1177 47 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.80it/s] all 1629 1629 0.042 0.259 0.0193 0.0166 Epoch gpu_mem box obj cls labels img_size 12/199 1.73G 0.01519 0.007818 0.1173 45 224: 100% 204/204 [02:29<00:00, 1.36it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.74it/s] all 1629 1629 0.0329 0.256 0.0205 0.0183 Epoch gpu_mem box obj cls labels img_size 13/199 1.73G 0.01499 0.007849 0.1167 51 224: 100% 204/204 [02:29<00:00, 1.37it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00, 1.65it/s] all 1629 1629 0.0174 0.313 0.0219 0.0195 Epoch gpu_mem box obj cls labels img_size 14/199 1.73G 0.01462 0.007693 0.1158 54 224: 100% 204/204 [02:30<00:00, 1.35it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.76it/s] all 1629 1629 0.0252 0.367 0.0229 0.0202 Epoch gpu_mem box obj cls labels img_size 15/199 1.73G 0.01501 0.007726 0.1151 55 224: 100% 204/204 [02:31<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.85it/s] all 1629 1629 0.0309 0.419 0.0254 0.0226 Epoch gpu_mem box obj cls labels img_size 16/199 1.73G 0.01484 0.007627 0.1143 42 224: 100% 204/204 [02:31<00:00, 1.35it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.75it/s] all 1629 1629 0.038 0.284 0.0308 0.0275 Epoch gpu_mem box obj cls labels img_size 17/199 1.73G 0.01501 0.007751 0.1133 45 224: 100% 204/204 [02:31<00:00, 1.35it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.82it/s] all 1629 1629 0.0476 0.382 0.033 0.0292 Epoch gpu_mem box obj cls labels img_size 18/199 1.73G 0.01517 0.00768 0.1124 45 224: 100% 204/204 [02:30<00:00, 1.35it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.76it/s] all 1629 1629 0.0223 0.418 0.0379 0.0332 Epoch gpu_mem box obj cls labels img_size 19/199 1.73G 0.01526 0.007644 0.1113 48 224: 100% 204/204 [02:29<00:00, 1.36it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.74it/s] all 1629 1629 0.0208 0.581 0.0426 0.037 Epoch gpu_mem box obj cls labels img_size 20/199 1.73G 0.01558 0.007689 0.1101 43 224: 100% 204/204 [02:30<00:00, 1.35it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.79it/s] all 1629 1629 0.059 0.411 0.0452 0.0394 Epoch gpu_mem box obj cls labels img_size 21/199 1.73G 0.01554 0.007742 0.1093 47 224: 100% 204/204 [02:29<00:00, 1.36it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.84it/s] all 1629 1629 0.0689 0.475 0.0529 0.0465 Epoch gpu_mem box obj cls labels img_size 22/199 1.73G 0.01553 0.007709 0.1085 53 224: 100% 204/204 [02:30<00:00, 1.36it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.80it/s] all 1629 1629 0.0632 0.563 0.0508 0.0451 Epoch gpu_mem box obj cls labels img_size 23/199 1.73G 0.01595 0.007666 0.1074 53 224: 100% 204/204 [02:30<00:00, 1.36it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.83it/s] all 1629 1629 0.0706 0.569 0.0577 0.0507 Epoch gpu_mem box obj cls labels img_size 24/199 1.73G 0.01579 0.007645 0.1066 55 224: 100% 204/204 [02:30<00:00, 1.36it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.80it/s] all 1629 1629 0.263 0.396 0.0605 0.054 Epoch gpu_mem box obj cls labels img_size 25/199 1.73G 0.01588 0.007795 0.1059 58 224: 100% 204/204 [02:31<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.75it/s] all 1629 1629 0.156 0.428 0.0626 0.0552 Epoch gpu_mem box obj cls labels img_size 26/199 1.73G 0.01594 0.007714 0.1051 50 224: 100% 204/204 [02:29<00:00, 1.36it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00, 1.68it/s] all 1629 1629 0.212 0.426 0.0735 0.0655 Epoch gpu_mem box obj cls labels img_size 27/199 1.73G 0.0159 0.007667 0.1041 50 224: 100% 204/204 [02:31<00:00, 1.35it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.82it/s] all 1629 1629 0.215 0.417 0.0823 0.0727 Epoch gpu_mem box obj cls labels img_size 28/199 1.73G 0.01598 0.007682 0.1035 48 224: 100% 204/204 [02:28<00:00, 1.37it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.79it/s] all 1629 1629 0.168 0.48 0.0731 0.0643 Epoch gpu_mem box obj cls labels img_size 29/199 1.73G 0.01572 0.007725 0.103 44 224: 100% 204/204 [02:30<00:00, 1.35it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.80it/s] all 1629 1629 0.326 0.397 0.087 0.0773 Epoch gpu_mem box obj cls labels img_size 30/199 1.73G 0.01603 0.007726 0.1022 48 224: 100% 204/204 [02:29<00:00, 1.36it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.74it/s] all 1629 1629 0.217 0.476 0.0947 0.0833 Epoch gpu_mem box obj cls labels img_size 31/199 1.73G 0.01606 0.007825 0.1012 44 224: 100% 204/204 [02:29<00:00, 1.36it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.79it/s] all 1629 1629 0.213 0.482 0.102 0.0901 Epoch gpu_mem box obj cls labels img_size 32/199 1.73G 0.01617 0.00782 0.1006 59 224: 100% 204/204 [02:30<00:00, 1.35it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.75it/s] all 1629 1629 0.21 0.509 0.108 0.0958 Epoch gpu_mem box obj cls labels img_size 33/199 1.73G 0.01623 0.007797 0.09922 51 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00, 1.88it/s] all 1629 1629 0.348 0.349 0.115 0.101 Epoch gpu_mem box obj cls labels img_size 34/199 1.73G 0.0163 0.007879 0.09896 53 224: 100% 204/204 [02:28<00:00, 1.37it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00, 1.71it/s] all 1629 1629 0.394 0.366 0.127 0.112 Epoch gpu_mem box obj cls labels img_size 35/199 1.73G 0.01631 0.007851 0.09805 44 224: 100% 204/204 [02:28<00:00, 1.37it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.75it/s] all 1629 1629 0.308 0.429 0.123 0.11 Epoch gpu_mem box obj cls labels img_size 36/199 1.73G 0.01643 0.007845 0.09773 54 224: 100% 204/204 [02:30<00:00, 1.36it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.78it/s] all 1629 1629 0.433 0.32 0.135 0.12 Epoch gpu_mem box obj cls labels img_size 37/199 1.73G 0.01644 0.007851 0.09658 41 224: 100% 204/204 [02:30<00:00, 1.36it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.75it/s] all 1629 1629 0.367 0.379 0.146 0.129 Epoch gpu_mem box obj cls labels img_size 38/199 1.73G 0.01647 0.007915 0.09581 57 224: 100% 204/204 [02:30<00:00, 1.35it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.81it/s] all 1629 1629 0.291 0.47 0.153 0.137 Epoch gpu_mem box obj cls labels img_size 39/199 1.73G 0.01667 0.007907 0.09477 49 224: 100% 204/204 [02:29<00:00, 1.36it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.81it/s] all 1629 1629 0.278 0.509 0.179 0.161 Epoch gpu_mem box obj cls labels img_size 40/199 1.73G 0.01648 0.0079 0.09437 44 224: 100% 204/204 [02:31<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.73it/s] all 1629 1629 0.267 0.507 0.179 0.158 Epoch gpu_mem box obj cls labels img_size 41/199 1.73G 0.01676 0.007901 0.09345 46 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.81it/s] all 1629 1629 0.24 0.558 0.196 0.175 Epoch gpu_mem box obj cls labels img_size 42/199 1.73G 0.01667 0.007897 0.09249 50 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.77it/s] all 1629 1629 0.318 0.413 0.21 0.189 Epoch gpu_mem box obj cls labels img_size 43/199 1.73G 0.01683 0.007903 0.09186 58 224: 100% 204/204 [02:31<00:00, 1.35it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.77it/s] all 1629 1629 0.215 0.577 0.213 0.189 Epoch gpu_mem box obj cls labels img_size 44/199 1.73G 0.01678 0.007917 0.09101 52 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.80it/s] all 1629 1629 0.318 0.439 0.214 0.191 Epoch gpu_mem box obj cls labels img_size 45/199 1.73G 0.01698 0.007962 0.09026 50 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.74it/s] all 1629 1629 0.289 0.479 0.234 0.208 Epoch gpu_mem box obj cls labels img_size 46/199 1.73G 0.01699 0.007985 0.08948 44 224: 100% 204/204 [02:31<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.84it/s] all 1629 1629 0.237 0.585 0.247 0.221 Epoch gpu_mem box obj cls labels img_size 47/199 1.73G 0.01706 0.007957 0.08891 47 224: 100% 204/204 [02:31<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.75it/s] all 1629 1629 0.355 0.441 0.262 0.235 Epoch gpu_mem box obj cls labels img_size 48/199 1.73G 0.01695 0.007903 0.08811 50 224: 100% 204/204 [02:31<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.76it/s] all 1629 1629 0.278 0.526 0.264 0.237 Epoch gpu_mem box obj cls labels img_size 49/199 1.73G 0.01714 0.007993 0.08738 54 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.80it/s] all 1629 1629 0.276 0.543 0.286 0.256 Epoch gpu_mem box obj cls labels img_size 50/199 1.73G 0.01714 0.007941 0.08676 48 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00, 1.92it/s] all 1629 1629 0.346 0.509 0.296 0.264 Epoch gpu_mem box obj cls labels img_size 51/199 1.73G 0.01714 0.007945 0.08566 55 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.83it/s] all 1629 1629 0.268 0.552 0.305 0.274 Epoch gpu_mem box obj cls labels img_size 52/199 1.73G 0.01704 0.007991 0.0854 53 224: 100% 204/204 [02:35<00:00, 1.32it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.75it/s] all 1629 1629 0.261 0.566 0.31 0.277 Epoch gpu_mem box obj cls labels img_size 53/199 1.73G 0.01725 0.008052 0.08479 50 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.82it/s] all 1629 1629 0.249 0.618 0.332 0.298 Epoch gpu_mem box obj cls labels img_size 54/199 1.73G 0.01706 0.007933 0.08397 46 224: 100% 204/204 [02:34<00:00, 1.32it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00, 1.89it/s] all 1629 1629 0.343 0.504 0.348 0.312 Epoch gpu_mem box obj cls labels img_size 55/199 1.73G 0.01717 0.007936 0.08329 47 224: 100% 204/204 [02:34<00:00, 1.32it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.82it/s] all 1629 1629 0.285 0.586 0.361 0.325 Epoch gpu_mem box obj cls labels img_size 56/199 1.73G 0.01727 0.007852 0.08224 47 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.73it/s] all 1629 1629 0.332 0.578 0.374 0.336 Epoch gpu_mem box obj cls labels img_size 57/199 1.73G 0.01735 0.008 0.08208 56 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00, 1.87it/s] all 1629 1629 0.426 0.495 0.396 0.357 Epoch gpu_mem box obj cls labels img_size 58/199 1.73G 0.01731 0.007908 0.08125 48 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.80it/s] all 1629 1629 0.361 0.55 0.405 0.366 Epoch gpu_mem box obj cls labels img_size 59/199 1.73G 0.01724 0.007919 0.08052 49 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.80it/s] all 1629 1629 0.375 0.568 0.414 0.374 Epoch gpu_mem box obj cls labels img_size 60/199 1.73G 0.01757 0.007998 0.07989 43 224: 100% 204/204 [02:32<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.77it/s] all 1629 1629 0.327 0.591 0.407 0.364 Epoch gpu_mem box obj cls labels img_size 61/199 1.73G 0.01746 0.00798 0.07928 43 224: 100% 204/204 [02:31<00:00, 1.35it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00, 1.72it/s] all 1629 1629 0.397 0.544 0.426 0.386 Epoch gpu_mem box obj cls labels img_size 62/199 1.73G 0.01746 0.007861 0.07871 56 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.75it/s] all 1629 1629 0.355 0.591 0.446 0.404 Epoch gpu_mem box obj cls labels img_size 63/199 1.73G 0.01769 0.007898 0.07762 45 224: 100% 204/204 [02:34<00:00, 1.32it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.76it/s] all 1629 1629 0.36 0.58 0.463 0.415 Epoch gpu_mem box obj cls labels img_size 64/199 1.73G 0.01783 0.007961 0.07768 45 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.84it/s] all 1629 1629 0.371 0.6 0.468 0.421 Epoch gpu_mem box obj cls labels img_size 65/199 1.73G 0.01755 0.007911 0.07736 46 224: 100% 204/204 [02:34<00:00, 1.32it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00, 1.96it/s] all 1629 1629 0.403 0.616 0.49 0.441 Epoch gpu_mem box obj cls labels img_size 66/199 1.73G 0.01764 0.007978 0.07663 45 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.79it/s] all 1629 1629 0.367 0.639 0.494 0.445 Epoch gpu_mem box obj cls labels img_size 67/199 1.73G 0.01771 0.00806 0.07532 49 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00, 1.92it/s] all 1629 1629 0.381 0.635 0.513 0.463 Epoch gpu_mem box obj cls labels img_size 68/199 1.73G 0.01764 0.008013 0.07472 44 224: 100% 204/204 [02:34<00:00, 1.32it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00, 1.87it/s] all 1629 1629 0.378 0.65 0.517 0.468 Epoch gpu_mem box obj cls labels img_size 69/199 1.73G 0.01779 0.007938 0.07363 48 224: 100% 204/204 [02:33<00:00, 1.32it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.80it/s] all 1629 1629 0.523 0.547 0.53 0.481 Epoch gpu_mem box obj cls labels img_size 70/199 1.73G 0.01788 0.008068 0.07366 48 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00, 1.73it/s] all 1629 1629 0.455 0.603 0.533 0.483 Epoch gpu_mem box obj cls labels img_size 71/199 1.73G 0.01762 0.007976 0.07261 48 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00, 1.65it/s] all 1629 1629 0.464 0.606 0.539 0.486 Epoch gpu_mem box obj cls labels img_size 72/199 1.73G 0.01782 0.008015 0.07225 52 224: 100% 204/204 [02:32<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00, 1.69it/s] all 1629 1629 0.492 0.595 0.562 0.505 Epoch gpu_mem box obj cls labels img_size 73/199 1.73G 0.01783 0.007993 0.07187 47 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.78it/s] all 1629 1629 0.483 0.622 0.573 0.518 Epoch gpu_mem box obj cls labels img_size 74/199 1.73G 0.01781 0.00801 0.07154 58 224: 100% 204/204 [02:35<00:00, 1.31it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00, 1.92it/s] all 1629 1629 0.497 0.626 0.582 0.527 Epoch gpu_mem box obj cls labels img_size 75/199 1.73G 0.01784 0.007937 0.07039 55 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.80it/s] all 1629 1629 0.526 0.608 0.592 0.536 Epoch gpu_mem box obj cls labels img_size 76/199 1.73G 0.01809 0.008141 0.07021 45 224: 100% 204/204 [02:31<00:00, 1.35it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.78it/s] all 1629 1629 0.464 0.672 0.595 0.538 Epoch gpu_mem box obj cls labels img_size 77/199 1.73G 0.01787 0.007976 0.06902 51 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.77it/s] all 1629 1629 0.515 0.636 0.599 0.541 Epoch gpu_mem box obj cls labels img_size 78/199 1.73G 0.01789 0.00801 0.06895 61 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.78it/s] all 1629 1629 0.476 0.665 0.605 0.548 Epoch gpu_mem box obj cls labels img_size 79/199 1.73G 0.01786 0.007944 0.06823 53 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.78it/s] all 1629 1629 0.508 0.669 0.622 0.563 Epoch gpu_mem box obj cls labels img_size 80/199 1.73G 0.01789 0.007962 0.0681 40 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.74it/s] all 1629 1629 0.537 0.641 0.633 0.572 Epoch gpu_mem box obj cls labels img_size 81/199 1.73G 0.01791 0.007976 0.0673 54 224: 100% 204/204 [02:31<00:00, 1.35it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.79it/s] all 1629 1629 0.536 0.648 0.638 0.577 Epoch gpu_mem box obj cls labels img_size 82/199 1.73G 0.01789 0.008019 0.06695 41 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.80it/s] all 1629 1629 0.504 0.712 0.65 0.59 Epoch gpu_mem box obj cls labels img_size 83/199 1.73G 0.01799 0.007968 0.0657 53 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.77it/s] all 1629 1629 0.484 0.739 0.659 0.595 Epoch gpu_mem box obj cls labels img_size 84/199 1.73G 0.01798 0.008016 0.06558 41 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.84it/s] all 1629 1629 0.495 0.715 0.658 0.596 Epoch gpu_mem box obj cls labels img_size 85/199 1.73G 0.01794 0.008048 0.06518 47 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.75it/s] all 1629 1629 0.469 0.756 0.668 0.605 Epoch gpu_mem box obj cls labels img_size 86/199 1.73G 0.01806 0.007973 0.0642 50 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.75it/s] all 1629 1629 0.591 0.679 0.678 0.614 Epoch gpu_mem box obj cls labels img_size 87/199 1.73G 0.01797 0.007965 0.06415 51 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.76it/s] all 1629 1629 0.585 0.679 0.685 0.622 Epoch gpu_mem box obj cls labels img_size 88/199 1.73G 0.01807 0.007981 0.06383 49 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.78it/s] all 1629 1629 0.489 0.755 0.69 0.625 Epoch gpu_mem box obj cls labels img_size 89/199 1.73G 0.01794 0.007989 0.06326 52 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.74it/s] all 1629 1629 0.471 0.781 0.698 0.634 Epoch gpu_mem box obj cls labels img_size 90/199 1.73G 0.01777 0.00793 0.06251 47 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.83it/s] all 1629 1629 0.522 0.736 0.699 0.636 Epoch gpu_mem box obj cls labels img_size 91/199 1.73G 0.01804 0.007902 0.06179 50 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.78it/s] all 1629 1629 0.555 0.732 0.713 0.649 Epoch gpu_mem box obj cls labels img_size 92/199 1.73G 0.01805 0.008036 0.06141 52 224: 100% 204/204 [02:31<00:00, 1.35it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.76it/s] all 1629 1629 0.588 0.697 0.714 0.65 Epoch gpu_mem box obj cls labels img_size 93/199 1.73G 0.01812 0.007914 0.06063 46 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.80it/s] all 1629 1629 0.574 0.715 0.715 0.651 Epoch gpu_mem box obj cls labels img_size 94/199 1.73G 0.01795 0.007946 0.06061 57 224: 100% 204/204 [02:31<00:00, 1.35it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.79it/s] all 1629 1629 0.579 0.723 0.723 0.658 Epoch gpu_mem box obj cls labels img_size 95/199 1.73G 0.01807 0.007848 0.05923 50 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00, 1.72it/s] all 1629 1629 0.592 0.731 0.728 0.665 Epoch gpu_mem box obj cls labels img_size 96/199 1.73G 0.01791 0.008023 0.05915 42 224: 100% 204/204 [02:34<00:00, 1.32it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.83it/s] all 1629 1629 0.557 0.757 0.736 0.672 Epoch gpu_mem box obj cls labels img_size 97/199 1.73G 0.01777 0.00787 0.05844 43 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.85it/s] all 1629 1629 0.6 0.734 0.745 0.682 Epoch gpu_mem box obj cls labels img_size 98/199 1.73G 0.01785 0.00782 0.05824 47 224: 100% 204/204 [02:35<00:00, 1.31it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.81it/s] all 1629 1629 0.624 0.725 0.749 0.682 Epoch gpu_mem box obj cls labels img_size 99/199 1.73G 0.01783 0.007909 0.05749 41 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.78it/s] all 1629 1629 0.636 0.733 0.757 0.69 Epoch gpu_mem box obj cls labels img_size 100/199 1.73G 0.0178 0.00792 0.05712 51 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00, 1.71it/s] all 1629 1629 0.609 0.756 0.76 0.695 Epoch gpu_mem box obj cls labels img_size 101/199 1.73G 0.01776 0.007919 0.05681 56 224: 100% 204/204 [02:35<00:00, 1.31it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00, 1.88it/s] all 1629 1629 0.644 0.734 0.761 0.696 Epoch gpu_mem box obj cls labels img_size 102/199 1.73G 0.01815 0.008006 0.05566 47 224: 100% 204/204 [02:34<00:00, 1.32it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.82it/s] all 1629 1629 0.649 0.734 0.764 0.7 Epoch gpu_mem box obj cls labels img_size 103/199 1.73G 0.01788 0.007937 0.05644 54 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00, 1.66it/s] all 1629 1629 0.64 0.748 0.771 0.704 Epoch gpu_mem box obj cls labels img_size 104/199 1.73G 0.01776 0.007878 0.05519 47 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.80it/s] all 1629 1629 0.625 0.774 0.774 0.708 Epoch gpu_mem box obj cls labels img_size 105/199 1.73G 0.01768 0.007875 0.05542 53 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00, 1.86it/s] all 1629 1629 0.627 0.768 0.776 0.709 Epoch gpu_mem box obj cls labels img_size 106/199 1.73G 0.0176 0.007773 0.0545 52 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.77it/s] all 1629 1629 0.634 0.774 0.784 0.715 Epoch gpu_mem box obj cls labels img_size 107/199 1.73G 0.01756 0.007809 0.05455 52 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.78it/s] all 1629 1629 0.614 0.801 0.787 0.718 Epoch gpu_mem box obj cls labels img_size 108/199 1.73G 0.01767 0.007878 0.05348 52 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.79it/s] all 1629 1629 0.63 0.792 0.791 0.723 Epoch gpu_mem box obj cls labels img_size 109/199 1.73G 0.01782 0.007859 0.05266 58 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00, 1.89it/s] all 1629 1629 0.627 0.795 0.795 0.727 Epoch gpu_mem box obj cls labels img_size 110/199 1.73G 0.01767 0.007814 0.05219 50 224: 100% 204/204 [02:34<00:00, 1.32it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.77it/s] all 1629 1629 0.659 0.776 0.799 0.731 Epoch gpu_mem box obj cls labels img_size 111/199 1.73G 0.01759 0.007875 0.05273 58 224: 100% 204/204 [02:31<00:00, 1.35it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.78it/s] all 1629 1629 0.686 0.769 0.804 0.735 Epoch gpu_mem box obj cls labels img_size 112/199 1.73G 0.01767 0.007867 0.05193 49 224: 100% 204/204 [02:34<00:00, 1.32it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.83it/s] all 1629 1629 0.707 0.763 0.807 0.738 Epoch gpu_mem box obj cls labels img_size 113/199 1.73G 0.01761 0.007742 0.05144 39 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.84it/s] all 1629 1629 0.715 0.766 0.809 0.74 Epoch gpu_mem box obj cls labels img_size 114/199 1.73G 0.01747 0.00782 0.05148 42 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.79it/s] all 1629 1629 0.724 0.769 0.812 0.744 Epoch gpu_mem box obj cls labels img_size 115/199 1.73G 0.01759 0.007872 0.05046 50 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.78it/s] all 1629 1629 0.695 0.787 0.815 0.746 Epoch gpu_mem box obj cls labels img_size 116/199 1.73G 0.01728 0.00778 0.05056 52 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.79it/s] all 1629 1629 0.699 0.789 0.818 0.748 Epoch gpu_mem box obj cls labels img_size 117/199 1.73G 0.01734 0.007827 0.05007 52 224: 100% 204/204 [02:34<00:00, 1.32it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.76it/s] all 1629 1629 0.733 0.777 0.823 0.751 Epoch gpu_mem box obj cls labels img_size 118/199 1.73G 0.01733 0.007795 0.04946 42 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.85it/s] all 1629 1629 0.724 0.785 0.825 0.753 Epoch gpu_mem box obj cls labels img_size 119/199 1.73G 0.01732 0.007777 0.04823 47 224: 100% 204/204 [02:31<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.73it/s] all 1629 1629 0.714 0.804 0.829 0.758 Epoch gpu_mem box obj cls labels img_size 120/199 1.73G 0.01715 0.007648 0.04842 50 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.76it/s] all 1629 1629 0.715 0.803 0.833 0.761 Epoch gpu_mem box obj cls labels img_size 121/199 1.73G 0.01729 0.007723 0.04748 47 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.77it/s] all 1629 1629 0.723 0.8 0.834 0.761 Epoch gpu_mem box obj cls labels img_size 122/199 1.73G 0.01733 0.007744 0.04742 57 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00, 1.73it/s] all 1629 1629 0.744 0.786 0.834 0.762 Epoch gpu_mem box obj cls labels img_size 123/199 1.73G 0.01734 0.007739 0.04702 51 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.74it/s] all 1629 1629 0.734 0.797 0.835 0.763 Epoch gpu_mem box obj cls labels img_size 124/199 1.73G 0.01707 0.007655 0.0469 49 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.76it/s] all 1629 1629 0.752 0.789 0.838 0.766 Epoch gpu_mem box obj cls labels img_size 125/199 1.73G 0.01698 0.007668 0.04659 47 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.81it/s] all 1629 1629 0.773 0.783 0.839 0.766 Epoch gpu_mem box obj cls labels img_size 126/199 1.73G 0.0171 0.007738 0.04598 54 224: 100% 204/204 [02:35<00:00, 1.31it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.80it/s] all 1629 1629 0.776 0.782 0.841 0.769 Epoch gpu_mem box obj cls labels img_size 127/199 1.73G 0.01707 0.007626 0.04543 54 224: 100% 204/204 [02:33<00:00, 1.32it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00, 1.63it/s] all 1629 1629 0.78 0.787 0.842 0.771 Epoch gpu_mem box obj cls labels img_size 128/199 1.73G 0.01699 0.007651 0.04558 48 224: 100% 204/204 [02:37<00:00, 1.30it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.74it/s] all 1629 1629 0.787 0.784 0.844 0.772 Epoch gpu_mem box obj cls labels img_size 129/199 1.73G 0.01684 0.007549 0.04496 53 224: 100% 204/204 [02:34<00:00, 1.32it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:16<00:00, 1.62it/s] all 1629 1629 0.787 0.789 0.846 0.776 Epoch gpu_mem box obj cls labels img_size 130/199 1.73G 0.0169 0.007587 0.04372 55 224: 100% 204/204 [02:35<00:00, 1.31it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.86it/s] all 1629 1629 0.769 0.804 0.847 0.776 Epoch gpu_mem box obj cls labels img_size 131/199 1.73G 0.01673 0.007659 0.04434 51 224: 100% 204/204 [02:35<00:00, 1.31it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00, 1.69it/s] all 1629 1629 0.778 0.802 0.849 0.777 Epoch gpu_mem box obj cls labels img_size 132/199 1.73G 0.01652 0.00763 0.04401 53 224: 100% 204/204 [02:35<00:00, 1.31it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.76it/s] all 1629 1629 0.795 0.794 0.85 0.778 Epoch gpu_mem box obj cls labels img_size 133/199 1.73G 0.01662 0.007587 0.04369 52 224: 100% 204/204 [02:34<00:00, 1.32it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.77it/s] all 1629 1629 0.797 0.796 0.853 0.781 Epoch gpu_mem box obj cls labels img_size 134/199 1.73G 0.01674 0.007632 0.04304 53 224: 100% 204/204 [02:36<00:00, 1.30it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.80it/s] all 1629 1629 0.8 0.797 0.856 0.784 Epoch gpu_mem box obj cls labels img_size 135/199 1.73G 0.01652 0.007645 0.04226 47 224: 100% 204/204 [02:35<00:00, 1.31it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.78it/s] all 1629 1629 0.767 0.823 0.857 0.786 Epoch gpu_mem box obj cls labels img_size 136/199 1.73G 0.01639 0.007479 0.04241 54 224: 100% 204/204 [02:35<00:00, 1.31it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.75it/s] all 1629 1629 0.804 0.797 0.859 0.787 Epoch gpu_mem box obj cls labels img_size 137/199 1.73G 0.01646 0.00753 0.04218 45 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.83it/s] all 1629 1629 0.801 0.8 0.861 0.789 Epoch gpu_mem box obj cls labels img_size 138/199 1.73G 0.01635 0.007481 0.0418 52 224: 100% 204/204 [02:34<00:00, 1.32it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.74it/s] all 1629 1629 0.797 0.807 0.862 0.79 Epoch gpu_mem box obj cls labels img_size 139/199 1.73G 0.01638 0.007541 0.04129 57 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00, 1.73it/s] all 1629 1629 0.801 0.81 0.863 0.79 Epoch gpu_mem box obj cls labels img_size 140/199 1.73G 0.01621 0.007493 0.0407 45 224: 100% 204/204 [02:34<00:00, 1.32it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.75it/s] all 1629 1629 0.806 0.806 0.865 0.792 Epoch gpu_mem box obj cls labels img_size 141/199 1.73G 0.01617 0.007479 0.0406 50 224: 100% 204/204 [02:34<00:00, 1.32it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.80it/s] all 1629 1629 0.81 0.808 0.866 0.793 Epoch gpu_mem box obj cls labels img_size 142/199 1.73G 0.0163 0.007437 0.03962 44 224: 100% 204/204 [02:32<00:00, 1.34it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.80it/s] all 1629 1629 0.812 0.807 0.867 0.794 Epoch gpu_mem box obj cls labels img_size 143/199 1.73G 0.01601 0.007462 0.0403 49 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:14<00:00, 1.80it/s] all 1629 1629 0.816 0.807 0.869 0.795 Epoch gpu_mem box obj cls labels img_size 144/199 1.73G 0.01613 0.007542 0.03942 54 224: 100% 204/204 [02:35<00:00, 1.31it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:13<00:00, 1.91it/s] all 1629 1629 0.812 0.81 0.871 0.797 Epoch gpu_mem box obj cls labels img_size 145/199 1.73G 0.01598 0.007501 0.03879 54 224: 100% 204/204 [02:33<00:00, 1.33it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 26/26 [00:15<00:00, 1.68it/s] all 1629 1629 0.808 0.813 0.871 0.798 Epoch gpu_mem box obj cls labels img_size 146/199 1.73G 0.01602 0.007438 0.03887 88 224: 96% 195/204 [02:26<00:04, 1.97it/s]
!ls
sample_data
!python val.py --img 224 --weights /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/yolov5/runs/train/yolo_car_classification6/weights/best.pt --data carData.yaml --task test --name yolo_car_class_det
val: data=/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/yolov5/data/carData.yaml, weights=['/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/yolov5/runs/train/yolo_car_classification6/weights/best.pt'], batch_size=32, imgsz=224, conf_thres=0.001, iou_thres=0.6, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=yolo_car_class_det, exist_ok=False, half=False, dnn=False YOLOv5 🚀 v6.0-200-g9708cf5 torch 1.10.0+cu111 CUDA:0 (Tesla P100-PCIE-16GB, 16281MiB) Fusing layers... Model Summary: 213 layers, 7538737 parameters, 0 gradients test: Scanning '/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/car_data_yolo/test/labels.cache' images and labels... 8040 found, 0 missing, 0 empty, 0 corrupt: 100% 8040/8040 [00:00<?, ?it/s] Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 252/252 [00:58<00:00, 4.32it/s] all 8040 8040 0.789 0.86 0.881 0.802 Speed: 0.0ms pre-process, 1.0ms inference, 1.0ms NMS per image at shape (32, 3, 224, 224) Results saved to runs/val/yolo_car_class_det5
!python export.py --weights runs/train/yolo_car_classification6/weights/best.pt --img 224 --batch 1 # export at 640x640 with batch size 1
export: data=data/coco128.yaml, weights=['runs/train/yolo_car_classification6/weights/best.pt'], imgsz=[224], batch_size=1, device=cpu, half=False, inplace=False, train=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=12, verbose=False, workspace=4, nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45, conf_thres=0.25, include=['torchscript', 'onnx'] YOLOv5 🚀 v6.0-200-g9708cf5 torch 1.10.0+cu111 CPU Fusing layers... Model Summary: 213 layers, 7538737 parameters, 0 gradients PyTorch: starting from runs/train/yolo_car_classification6/weights/best.pt (60.9 MB) TorchScript: starting export with torch 1.10.0+cu111... /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/yolov5/models/yolo.py:57: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! if self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]: /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/yolov5/models/yolo.py:57: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! if self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]: TorchScript: export success, saved as runs/train/yolo_car_classification6/weights/best.torchscript (30.5 MB) requirements: onnx not found and is required by YOLOv5, attempting auto-update... Collecting onnx Downloading onnx-1.10.2-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (12.7 MB) Requirement already satisfied: numpy>=1.16.6 in /usr/local/lib/python3.7/dist-packages (from onnx) (1.19.5) Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from onnx) (1.15.0) Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from onnx) (3.17.3) Requirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.7/dist-packages (from onnx) (3.10.0.2) Installing collected packages: onnx Successfully installed onnx-1.10.2 requirements: 1 package updated per ['onnx'] requirements: ⚠️ Restart runtime or rerun command for updates to take effect ONNX: starting export with onnx 1.10.2... /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/yolov5/models/yolo.py:57: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! if self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]: ONNX: export success, saved as runs/train/yolo_car_classification6/weights/best.onnx (30.2 MB) Export complete (8.51s) Results saved to /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/yolov5/runs/train/yolo_car_classification6/weights Visualize with https://netron.app Detect with `python detect.py --weights runs/train/yolo_car_classification6/weights/best.onnx` or `model = torch.hub.load('ultralytics/yolov5', 'custom', 'runs/train/yolo_car_classification6/weights/best.onnx') Validate with `python val.py --weights runs/train/yolo_car_classification6/weights/best.onnx`
IMAGE_PATHS = list(df_test_ann['filepath'])
IMAGE_PATHS
['/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Suzuki Aerio Sedan 2007/00001.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ferrari 458 Italia Convertible 2012/00002.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Jeep Patriot SUV 2012/00003.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Toyota Camry Sedan 2012/00004.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Tesla Model S Sedan 2012/00005.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chrysler Town and Country Minivan 2012/00006.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/GMC Terrain SUV 2012/00007.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Mercedes-Benz S-Class Sedan 2012/00008.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW X5 SUV 2007/00009.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet HHR SS 2010/00010.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Buick Verano Sedan 2012/00011.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ford Freestar Minivan 2007/00012.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ford E-Series Wagon Van 2012/00013.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Hyundai Elantra Sedan 2007/00014.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Dodge Caliber Wagon 2012/00015.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Cadillac CTS-V Sedan 2012/00016.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Land Rover Range Rover SUV 2012/00017.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW X6 SUV 2012/00018.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Audi S5 Coupe 2012/00019.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW X5 SUV 2007/00020.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Lamborghini Aventador Coupe 2012/00021.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Acura TL Type-S 2008/00022.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chrysler PT Cruiser Convertible 2008/00023.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ford Mustang Convertible 2007/00024.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Mercedes-Benz S-Class Sedan 2012/00025.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Volkswagen Golf Hatchback 1991/00026.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Nissan Juke Hatchback 2012/00027.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Volvo XC90 SUV 2007/00028.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Dodge Dakota Club Cab 2007/00029.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Acura TSX Sedan 2012/00030.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Mercedes-Benz E-Class Sedan 2012/00031.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Jeep Grand Cherokee SUV 2012/00032.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Toyota Corolla Sedan 2012/00033.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Bentley Continental Flying Spur Sedan 2007/00034.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW X6 SUV 2012/00035.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Acura ZDX Hatchback 2012/00036.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW 6 Series Convertible 2007/00037.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Silverado 1500 Extended Cab 2012/00038.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW 3 Series Wagon 2012/00039.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Mercedes-Benz Sprinter Van 2012/00040.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Spyker C8 Coupe 2009/00041.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ford F-450 Super Duty Crew Cab 2012/00042.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Acura TL Sedan 2012/00043.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Avalanche Crew Cab 2012/00044.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Volkswagen Golf Hatchback 1991/00045.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW M5 Sedan 2010/00046.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Silverado 1500 Regular Cab 2012/00047.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Dodge Caravan Minivan 1997/00048.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Audi TT RS Coupe 2012/00049.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Nissan Leaf Hatchback 2012/00050.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Hyundai Genesis Sedan 2012/00051.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Hyundai Sonata Hybrid Sedan 2012/00052.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Volkswagen Golf Hatchback 1991/00053.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Lincoln Town Car Sedan 2011/00054.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Suzuki Aerio Sedan 2007/00055.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/GMC Yukon Hybrid SUV 2012/00056.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Silverado 1500 Regular Cab 2012/00057.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Hyundai Veloster Hatchback 2012/00058.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Honda Accord Coupe 2012/00059.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW M6 Convertible 2010/00060.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW M5 Sedan 2010/00061.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/GMC Terrain SUV 2012/00062.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Nissan 240SX Coupe 1998/00063.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW X3 SUV 2012/00064.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Express Cargo Van 2007/00065.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Suzuki SX4 Hatchback 2012/00066.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Toyota Corolla Sedan 2012/00067.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Dodge Caliber Wagon 2012/00068.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Bentley Continental GT Coupe 2007/00069.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Audi 100 Wagon 1994/00070.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chrysler Aspen SUV 2009/00071.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Hyundai Genesis Sedan 2012/00072.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Cadillac CTS-V Sedan 2012/00073.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Hyundai Veloster Hatchback 2012/00074.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Nissan Leaf Hatchback 2012/00075.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/AM General Hummer SUV 2000/00076.jpg', '/content/drive/My 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'/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Geo Metro Convertible 1993/00087.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Daewoo Nubira Wagon 2002/00088.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Volkswagen Beetle Hatchback 2012/00089.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Dodge Challenger SRT8 2011/00090.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Cadillac CTS-V Sedan 2012/00091.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Jeep Wrangler SUV 2012/00092.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Daewoo Nubira Wagon 2002/00093.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Dodge Charger Sedan 2012/00094.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Toyota Camry Sedan 2012/00095.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test 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2012/00964.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Toyota Corolla Sedan 2012/00965.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Honda Odyssey Minivan 2012/00966.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW ActiveHybrid 5 Sedan 2012/00967.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/FIAT 500 Abarth 2012/00968.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Dodge Durango SUV 2007/00969.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW 3 Series Sedan 2012/00970.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Mercedes-Benz C-Class Sedan 2012/00971.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/McLaren MP4-12C Coupe 2012/00972.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Corvette ZR1 2012/00973.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Silverado 1500 Classic Extended Cab 2007/00974.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Rolls-Royce Phantom Sedan 2012/00975.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Acura ZDX Hatchback 2012/00976.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Volkswagen Golf Hatchback 2012/00977.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW M5 Sedan 2010/00978.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Aston Martin Virage Coupe 2012/00979.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Mitsubishi Lancer Sedan 2012/00980.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Corvette Ron Fellows Edition Z06 2007/00981.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Hyundai Veracruz SUV 2012/00982.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Dodge Journey SUV 2012/00983.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chrysler Town and Country Minivan 2012/00984.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Cobalt SS 2010/00985.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chrysler Town and Country Minivan 2012/00986.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Honda Odyssey Minivan 2007/00987.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Honda Odyssey Minivan 2012/00988.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Toyota 4Runner SUV 2012/00989.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Fisker Karma Sedan 2012/00990.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Acura ZDX Hatchback 2012/00991.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Spyker C8 Convertible 2009/00992.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Sonic Sedan 2012/00993.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Dodge Caliber Wagon 2012/00994.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet Impala Sedan 2007/00995.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ferrari 458 Italia Convertible 2012/00996.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Audi TTS Coupe 2012/00997.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Maybach Landaulet Convertible 2012/00998.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ford F-150 Regular Cab 2012/00999.jpg', '/content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Toyota Camry Sedan 2012/01000.jpg', ...]
os.getcwd()
'/content/drive/MyDrive/capstone/tensorflow/models/research'
%cd ..
/content/drive/MyDrive/capstone/tensorflow
%cd workspace/training-ssd/exported-models/my_model/
/content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd/exported-models/my_model
#PATH_TO_MODEL_DIR = "workspace/training-sdd/exported-models/my_model"
#os.chdir(PATH_TO_MODEL_DIR)
import time
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as viz_utils
#PATH_TO_SAVED_MODEL = PATH_TO_MODEL_DIR + "saved_model"
print('Loading model...', end='')
start_time = time.time()
# Load saved model and build the detection function
detect_fn = tf.saved_model.load("saved_model")
end_time = time.time()
elapsed_time = end_time - start_time
print('Done! Took {} seconds'.format(elapsed_time))
Loading model...Done! Took 23.845096588134766 seconds
def download_labels(filename):
#base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/'
#label_dir = tf.keras.utils.get_file(fname=filename,
# origin=base_url + filename,
# untar=False)
label_dir = os.path.join("/content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd/annotations",filename)
return str(label_dir)
LABEL_FILENAME = 'label_map.pbtxt'
PATH_TO_LABELS = download_labels(LABEL_FILENAME)
PATH_TO_LABELS
'/content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd/annotations/label_map.pbtxt'
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS,
use_display_name=True)
category_index
{1: {'id': 1, 'name': 'AM General Hummer SUV 2000'},
2: {'id': 2, 'name': 'Acura RL Sedan 2012'},
3: {'id': 3, 'name': 'Acura TL Sedan 2012'},
4: {'id': 4, 'name': 'Acura TL Type-S 2008'},
5: {'id': 5, 'name': 'Acura TSX Sedan 2012'},
6: {'id': 6, 'name': 'Acura Integra Type R 2001'},
7: {'id': 7, 'name': 'Acura ZDX Hatchback 2012'},
8: {'id': 8, 'name': 'Aston Martin V8 Vantage Convertible 2012'},
9: {'id': 9, 'name': 'Aston Martin V8 Vantage Coupe 2012'},
10: {'id': 10, 'name': 'Aston Martin Virage Convertible 2012'},
11: {'id': 11, 'name': 'Aston Martin Virage Coupe 2012'},
12: {'id': 12, 'name': 'Audi RS 4 Convertible 2008'},
13: {'id': 13, 'name': 'Audi A5 Coupe 2012'},
14: {'id': 14, 'name': 'Audi TTS Coupe 2012'},
15: {'id': 15, 'name': 'Audi R8 Coupe 2012'},
16: {'id': 16, 'name': 'Audi V8 Sedan 1994'},
17: {'id': 17, 'name': 'Audi 100 Sedan 1994'},
18: {'id': 18, 'name': 'Audi 100 Wagon 1994'},
19: {'id': 19, 'name': 'Audi TT Hatchback 2011'},
20: {'id': 20, 'name': 'Audi S6 Sedan 2011'},
21: {'id': 21, 'name': 'Audi S5 Convertible 2012'},
22: {'id': 22, 'name': 'Audi S5 Coupe 2012'},
23: {'id': 23, 'name': 'Audi S4 Sedan 2012'},
24: {'id': 24, 'name': 'Audi S4 Sedan 2007'},
25: {'id': 25, 'name': 'Audi TT RS Coupe 2012'},
26: {'id': 26, 'name': 'BMW ActiveHybrid 5 Sedan 2012'},
27: {'id': 27, 'name': 'BMW 1 Series Convertible 2012'},
28: {'id': 28, 'name': 'BMW 1 Series Coupe 2012'},
29: {'id': 29, 'name': 'BMW 3 Series Sedan 2012'},
30: {'id': 30, 'name': 'BMW 3 Series Wagon 2012'},
31: {'id': 31, 'name': 'BMW 6 Series Convertible 2007'},
32: {'id': 32, 'name': 'BMW X5 SUV 2007'},
33: {'id': 33, 'name': 'BMW X6 SUV 2012'},
34: {'id': 34, 'name': 'BMW M3 Coupe 2012'},
35: {'id': 35, 'name': 'BMW M5 Sedan 2010'},
36: {'id': 36, 'name': 'BMW M6 Convertible 2010'},
37: {'id': 37, 'name': 'BMW X3 SUV 2012'},
38: {'id': 38, 'name': 'BMW Z4 Convertible 2012'},
39: {'id': 39,
'name': 'Bentley Continental Supersports Conv. Convertible 2012'},
40: {'id': 40, 'name': 'Bentley Arnage Sedan 2009'},
41: {'id': 41, 'name': 'Bentley Mulsanne Sedan 2011'},
42: {'id': 42, 'name': 'Bentley Continental GT Coupe 2012'},
43: {'id': 43, 'name': 'Bentley Continental GT Coupe 2007'},
44: {'id': 44, 'name': 'Bentley Continental Flying Spur Sedan 2007'},
45: {'id': 45, 'name': 'Bugatti Veyron 16.4 Convertible 2009'},
46: {'id': 46, 'name': 'Bugatti Veyron 16.4 Coupe 2009'},
47: {'id': 47, 'name': 'Buick Regal GS 2012'},
48: {'id': 48, 'name': 'Buick Rainier SUV 2007'},
49: {'id': 49, 'name': 'Buick Verano Sedan 2012'},
50: {'id': 50, 'name': 'Buick Enclave SUV 2012'},
51: {'id': 51, 'name': 'Cadillac CTS-V Sedan 2012'},
52: {'id': 52, 'name': 'Cadillac SRX SUV 2012'},
53: {'id': 53, 'name': 'Cadillac Escalade EXT Crew Cab 2007'},
54: {'id': 54, 'name': 'Chevrolet Silverado 1500 Hybrid Crew Cab 2012'},
55: {'id': 55, 'name': 'Chevrolet Corvette Convertible 2012'},
56: {'id': 56, 'name': 'Chevrolet Corvette ZR1 2012'},
57: {'id': 57, 'name': 'Chevrolet Corvette Ron Fellows Edition Z06 2007'},
58: {'id': 58, 'name': 'Chevrolet Traverse SUV 2012'},
59: {'id': 59, 'name': 'Chevrolet Camaro Convertible 2012'},
60: {'id': 60, 'name': 'Chevrolet HHR SS 2010'},
61: {'id': 61, 'name': 'Chevrolet Impala Sedan 2007'},
62: {'id': 62, 'name': 'Chevrolet Tahoe Hybrid SUV 2012'},
63: {'id': 63, 'name': 'Chevrolet Sonic Sedan 2012'},
64: {'id': 64, 'name': 'Chevrolet Express Cargo Van 2007'},
65: {'id': 65, 'name': 'Chevrolet Avalanche Crew Cab 2012'},
66: {'id': 66, 'name': 'Chevrolet Cobalt SS 2010'},
67: {'id': 67, 'name': 'Chevrolet Malibu Hybrid Sedan 2010'},
68: {'id': 68, 'name': 'Chevrolet TrailBlazer SS 2009'},
69: {'id': 69, 'name': 'Chevrolet Silverado 2500HD Regular Cab 2012'},
70: {'id': 70, 'name': 'Chevrolet Silverado 1500 Classic Extended Cab 2007'},
71: {'id': 71, 'name': 'Chevrolet Express Van 2007'},
72: {'id': 72, 'name': 'Chevrolet Monte Carlo Coupe 2007'},
73: {'id': 73, 'name': 'Chevrolet Malibu Sedan 2007'},
74: {'id': 74, 'name': 'Chevrolet Silverado 1500 Extended Cab 2012'},
75: {'id': 75, 'name': 'Chevrolet Silverado 1500 Regular Cab 2012'},
76: {'id': 76, 'name': 'Chrysler Aspen SUV 2009'},
77: {'id': 77, 'name': 'Chrysler Sebring Convertible 2010'},
78: {'id': 78, 'name': 'Chrysler Town and Country Minivan 2012'},
79: {'id': 79, 'name': 'Chrysler 300 SRT-8 2010'},
80: {'id': 80, 'name': 'Chrysler Crossfire Convertible 2008'},
81: {'id': 81, 'name': 'Chrysler PT Cruiser Convertible 2008'},
82: {'id': 82, 'name': 'Daewoo Nubira Wagon 2002'},
83: {'id': 83, 'name': 'Dodge Caliber Wagon 2012'},
84: {'id': 84, 'name': 'Dodge Caliber Wagon 2007'},
85: {'id': 85, 'name': 'Dodge Caravan Minivan 1997'},
86: {'id': 86, 'name': 'Dodge Ram Pickup 3500 Crew Cab 2010'},
87: {'id': 87, 'name': 'Dodge Ram Pickup 3500 Quad Cab 2009'},
88: {'id': 88, 'name': 'Dodge Sprinter Cargo Van 2009'},
89: {'id': 89, 'name': 'Dodge Journey SUV 2012'},
90: {'id': 90, 'name': 'Dodge Dakota Crew Cab 2010'},
91: {'id': 91, 'name': 'Dodge Dakota Club Cab 2007'},
92: {'id': 92, 'name': 'Dodge Magnum Wagon 2008'},
93: {'id': 93, 'name': 'Dodge Challenger SRT8 2011'},
94: {'id': 94, 'name': 'Dodge Durango SUV 2012'},
95: {'id': 95, 'name': 'Dodge Durango SUV 2007'},
96: {'id': 96, 'name': 'Dodge Charger Sedan 2012'},
97: {'id': 97, 'name': 'Dodge Charger SRT-8 2009'},
98: {'id': 98, 'name': 'Eagle Talon Hatchback 1998'},
99: {'id': 99, 'name': 'FIAT 500 Abarth 2012'},
100: {'id': 100, 'name': 'FIAT 500 Convertible 2012'},
101: {'id': 101, 'name': 'Ferrari FF Coupe 2012'},
102: {'id': 102, 'name': 'Ferrari California Convertible 2012'},
103: {'id': 103, 'name': 'Ferrari 458 Italia Convertible 2012'},
104: {'id': 104, 'name': 'Ferrari 458 Italia Coupe 2012'},
105: {'id': 105, 'name': 'Fisker Karma Sedan 2012'},
106: {'id': 106, 'name': 'Ford F-450 Super Duty Crew Cab 2012'},
107: {'id': 107, 'name': 'Ford Mustang Convertible 2007'},
108: {'id': 108, 'name': 'Ford Freestar Minivan 2007'},
109: {'id': 109, 'name': 'Ford Expedition EL SUV 2009'},
110: {'id': 110, 'name': 'Ford Edge SUV 2012'},
111: {'id': 111, 'name': 'Ford Ranger SuperCab 2011'},
112: {'id': 112, 'name': 'Ford GT Coupe 2006'},
113: {'id': 113, 'name': 'Ford F-150 Regular Cab 2012'},
114: {'id': 114, 'name': 'Ford F-150 Regular Cab 2007'},
115: {'id': 115, 'name': 'Ford Focus Sedan 2007'},
116: {'id': 116, 'name': 'Ford E-Series Wagon Van 2012'},
117: {'id': 117, 'name': 'Ford Fiesta Sedan 2012'},
118: {'id': 118, 'name': 'GMC Terrain SUV 2012'},
119: {'id': 119, 'name': 'GMC Savana Van 2012'},
120: {'id': 120, 'name': 'GMC Yukon Hybrid SUV 2012'},
121: {'id': 121, 'name': 'GMC Acadia SUV 2012'},
122: {'id': 122, 'name': 'GMC Canyon Extended Cab 2012'},
123: {'id': 123, 'name': 'Geo Metro Convertible 1993'},
124: {'id': 124, 'name': 'HUMMER H3T Crew Cab 2010'},
125: {'id': 125, 'name': 'HUMMER H2 SUT Crew Cab 2009'},
126: {'id': 126, 'name': 'Honda Odyssey Minivan 2012'},
127: {'id': 127, 'name': 'Honda Odyssey Minivan 2007'},
128: {'id': 128, 'name': 'Honda Accord Coupe 2012'},
129: {'id': 129, 'name': 'Honda Accord Sedan 2012'},
130: {'id': 130, 'name': 'Hyundai Veloster Hatchback 2012'},
131: {'id': 131, 'name': 'Hyundai Santa Fe SUV 2012'},
132: {'id': 132, 'name': 'Hyundai Tucson SUV 2012'},
133: {'id': 133, 'name': 'Hyundai Veracruz SUV 2012'},
134: {'id': 134, 'name': 'Hyundai Sonata Hybrid Sedan 2012'},
135: {'id': 135, 'name': 'Hyundai Elantra Sedan 2007'},
136: {'id': 136, 'name': 'Hyundai Accent Sedan 2012'},
137: {'id': 137, 'name': 'Hyundai Genesis Sedan 2012'},
138: {'id': 138, 'name': 'Hyundai Sonata Sedan 2012'},
139: {'id': 139, 'name': 'Hyundai Elantra Touring Hatchback 2012'},
140: {'id': 140, 'name': 'Hyundai Azera Sedan 2012'},
141: {'id': 141, 'name': 'Infiniti G Coupe IPL 2012'},
142: {'id': 142, 'name': 'Infiniti QX56 SUV 2011'},
143: {'id': 143, 'name': 'Isuzu Ascender SUV 2008'},
144: {'id': 144, 'name': 'Jaguar XK XKR 2012'},
145: {'id': 145, 'name': 'Jeep Patriot SUV 2012'},
146: {'id': 146, 'name': 'Jeep Wrangler SUV 2012'},
147: {'id': 147, 'name': 'Jeep Liberty SUV 2012'},
148: {'id': 148, 'name': 'Jeep Grand Cherokee SUV 2012'},
149: {'id': 149, 'name': 'Jeep Compass SUV 2012'},
150: {'id': 150, 'name': 'Lamborghini Reventon Coupe 2008'},
151: {'id': 151, 'name': 'Lamborghini Aventador Coupe 2012'},
152: {'id': 152, 'name': 'Lamborghini Gallardo LP 570-4 Superleggera 2012'},
153: {'id': 153, 'name': 'Lamborghini Diablo Coupe 2001'},
154: {'id': 154, 'name': 'Land Rover Range Rover SUV 2012'},
155: {'id': 155, 'name': 'Land Rover LR2 SUV 2012'},
156: {'id': 156, 'name': 'Lincoln Town Car Sedan 2011'},
157: {'id': 157, 'name': 'MINI Cooper Roadster Convertible 2012'},
158: {'id': 158, 'name': 'Maybach Landaulet Convertible 2012'},
159: {'id': 159, 'name': 'Mazda Tribute SUV 2011'},
160: {'id': 160, 'name': 'McLaren MP4-12C Coupe 2012'},
161: {'id': 161, 'name': 'Mercedes-Benz 300-Class Convertible 1993'},
162: {'id': 162, 'name': 'Mercedes-Benz C-Class Sedan 2012'},
163: {'id': 163, 'name': 'Mercedes-Benz SL-Class Coupe 2009'},
164: {'id': 164, 'name': 'Mercedes-Benz E-Class Sedan 2012'},
165: {'id': 165, 'name': 'Mercedes-Benz S-Class Sedan 2012'},
166: {'id': 166, 'name': 'Mercedes-Benz Sprinter Van 2012'},
167: {'id': 167, 'name': 'Mitsubishi Lancer Sedan 2012'},
168: {'id': 168, 'name': 'Nissan Leaf Hatchback 2012'},
169: {'id': 169, 'name': 'Nissan NV Passenger Van 2012'},
170: {'id': 170, 'name': 'Nissan Juke Hatchback 2012'},
171: {'id': 171, 'name': 'Nissan 240SX Coupe 1998'},
172: {'id': 172, 'name': 'Plymouth Neon Coupe 1999'},
173: {'id': 173, 'name': 'Porsche Panamera Sedan 2012'},
174: {'id': 174, 'name': 'Ram C-V Cargo Van Minivan 2012'},
175: {'id': 175,
'name': 'Rolls-Royce Phantom Drophead Coupe Convertible 2012'},
176: {'id': 176, 'name': 'Rolls-Royce Ghost Sedan 2012'},
177: {'id': 177, 'name': 'Rolls-Royce Phantom Sedan 2012'},
178: {'id': 178, 'name': 'Scion xD Hatchback 2012'},
179: {'id': 179, 'name': 'Spyker C8 Convertible 2009'},
180: {'id': 180, 'name': 'Spyker C8 Coupe 2009'},
181: {'id': 181, 'name': 'Suzuki Aerio Sedan 2007'},
182: {'id': 182, 'name': 'Suzuki Kizashi Sedan 2012'},
183: {'id': 183, 'name': 'Suzuki SX4 Hatchback 2012'},
184: {'id': 184, 'name': 'Suzuki SX4 Sedan 2012'},
185: {'id': 185, 'name': 'Tesla Model S Sedan 2012'},
186: {'id': 186, 'name': 'Toyota Sequoia SUV 2012'},
187: {'id': 187, 'name': 'Toyota Camry Sedan 2012'},
188: {'id': 188, 'name': 'Toyota Corolla Sedan 2012'},
189: {'id': 189, 'name': 'Toyota 4Runner SUV 2012'},
190: {'id': 190, 'name': 'Volkswagen Golf Hatchback 2012'},
191: {'id': 191, 'name': 'Volkswagen Golf Hatchback 1991'},
192: {'id': 192, 'name': 'Volkswagen Beetle Hatchback 2012'},
193: {'id': 193, 'name': 'Volvo C30 Hatchback 2012'},
194: {'id': 194, 'name': 'Volvo 240 Sedan 1993'},
195: {'id': 195, 'name': 'Volvo XC90 SUV 2007'},
196: {'id': 196, 'name': 'smart fortwo Convertible 2012'}}
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore') # Suppress Matplotlib warnings
def load_image_into_numpy_array(path):
"""Load an image from file into a numpy array.
Puts image into numpy array to feed into tensorflow graph.
Note that by convention we put it into a numpy array with shape
(height, width, channels), where channels=3 for RGB.
Args:
path: the file path to the image
Returns:
uint8 numpy array with shape (img_height, img_width, 3)
"""
return np.array(Image.open(path))
for image_path in IMAGE_PATHS:
print('Running inference for {}... '.format(image_path), end='')
image_np = load_image_into_numpy_array(image_path)
# Things to try:
# Flip horizontally
# image_np = np.fliplr(image_np).copy()
# Convert image to grayscale
# image_np = np.tile(
# np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8)
# The input needs to be a tensor, convert it using `tf.convert_to_tensor`.
input_tensor = tf.convert_to_tensor(image_np)
# The model expects a batch of images, so add an axis with `tf.newaxis`.
input_tensor = input_tensor[tf.newaxis, ...]
# input_tensor = np.expand_dims(image_np, 0)
detections = detect_fn(input_tensor)
# All outputs are batches tensors.
# Convert to numpy arrays, and take index [0] to remove the batch dimension.
# We're only interested in the first num_detections.
num_detections = int(detections.pop('num_detections'))
detections = {key: value[0, :num_detections].numpy()
for key, value in detections.items()}
detections['num_detections'] = num_detections
# detection_classes should be ints.
detections['detection_classes'] = detections['detection_classes'].astype(np.int64)
image_np_with_detections = image_np.copy()
viz_utils.visualize_boxes_and_labels_on_image_array(
image_np_with_detections,
detections['detection_boxes'],
detections['detection_classes'],
detections['detection_scores'],
category_index,
use_normalized_coordinates=True,
max_boxes_to_draw=200,
min_score_thresh=.30,
agnostic_mode=False)
plt.figure()
plt.imshow(image_np_with_detections)
print('Done')
plt.show()
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Suzuki Aerio Sedan 2007/00001.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ferrari 458 Italia Convertible 2012/00002.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Jeep Patriot SUV 2012/00003.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Toyota Camry Sedan 2012/00004.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Tesla Model S Sedan 2012/00005.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chrysler Town and Country Minivan 2012/00006.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/GMC Terrain SUV 2012/00007.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Mercedes-Benz S-Class Sedan 2012/00008.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW X5 SUV 2007/00009.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet HHR SS 2010/00010.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Buick Verano Sedan 2012/00011.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ford Freestar Minivan 2007/00012.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ford E-Series Wagon Van 2012/00013.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Hyundai Elantra Sedan 2007/00014.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Dodge Caliber Wagon 2012/00015.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Cadillac CTS-V Sedan 2012/00016.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Land Rover Range Rover SUV 2012/00017.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW X6 SUV 2012/00018.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Audi S5 Coupe 2012/00019.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW X5 SUV 2007/00020.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Lamborghini Aventador Coupe 2012/00021.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Acura TL Type-S 2008/00022.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chrysler PT Cruiser Convertible 2008/00023.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ford Mustang Convertible 2007/00024.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Mercedes-Benz S-Class Sedan 2012/00025.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Volkswagen Golf Hatchback 1991/00026.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Nissan Juke Hatchback 2012/00027.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Volvo XC90 SUV 2007/00028.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Dodge Dakota Club Cab 2007/00029.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Acura TSX Sedan 2012/00030.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Mercedes-Benz E-Class Sedan 2012/00031.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Jeep Grand Cherokee SUV 2012/00032.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Toyota Corolla Sedan 2012/00033.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Bentley Continental Flying Spur Sedan 2007/00034.jpg...
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-70-5c2ce9f3fedc> in <module>() 41 42 # input_tensor = np.expand_dims(image_np, 0) ---> 43 detections = detect_fn(input_tensor) 44 45 # All outputs are batches tensors. /usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/load.py in _call_attribute(instance, *args, **kwargs) 699 700 def _call_attribute(instance, *args, **kwargs): --> 701 return instance.__call__(*args, **kwargs) 702 703 /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/traceback_utils.py in error_handler(*args, **kwargs) 151 except Exception as e: 152 filtered_tb = _process_traceback_frames(e.__traceback__) --> 153 raise e.with_traceback(filtered_tb) from None 154 finally: 155 del filtered_tb /usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py in _convert_inputs_to_signature(inputs, input_signature, flat_input_signature) 2980 flat_input_signature, 2981 flatten_inputs)): -> 2982 raise ValueError("Python inputs incompatible with input_signature:\n" 2983 f"{format_error_message(inputs, input_signature)}.") 2984 ValueError: Python inputs incompatible with input_signature: inputs: ( tf.Tensor( [[[ 87 87 87 ... 151 151 151] [ 86 85 85 ... 152 152 152] [ 82 82 82 ... 152 152 152] ... [133 133 133 ... 133 133 133] [133 133 133 ... 133 133 133] [133 133 133 ... 133 133 133]]], shape=(1, 400, 600), dtype=uint8)) input_signature: ( TensorSpec(shape=(1, None, None, 3), dtype=tf.uint8, name='input_tensor')).
%cd /content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd/
/content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd
os.getcwd()
'/content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd'
!ls
annotations images 'pre-trained models' exported-models model_main_tf2.py exporter_main_v2.py models
%cd exported-models/my_model_rcnn/
/content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd/exported-models/my_model_rcnn
!ls
checkpoint pipeline.config saved_model
import time
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as viz_utils
print('Loading model...', end='')
start_time = time.time()
# Load saved model and build the detection function
detect_fn = tf.saved_model.load("saved_model")
end_time = time.time()
elapsed_time = end_time - start_time
print('Done! Took {} seconds'.format(elapsed_time))
Loading model...Done! Took 43.729407787323 seconds
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore') # Suppress Matplotlib warnings
for image_path in IMAGE_PATHS:
print('Running inference for {}... '.format(image_path), end='')
image_np = load_image_into_numpy_array(image_path)
# Things to try:
# Flip horizontally
# image_np = np.fliplr(image_np).copy()
# Convert image to grayscale
# image_np = np.tile(
# np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8)
# The input needs to be a tensor, convert it using `tf.convert_to_tensor`.
input_tensor = tf.convert_to_tensor(image_np)
# The model expects a batch of images, so add an axis with `tf.newaxis`.
input_tensor = input_tensor[tf.newaxis, ...]
# input_tensor = np.expand_dims(image_np, 0)
detections = detect_fn(input_tensor)
# All outputs are batches tensors.
# Convert to numpy arrays, and take index [0] to remove the batch dimension.
# We're only interested in the first num_detections.
num_detections = int(detections.pop('num_detections'))
detections = {key: value[0, :num_detections].numpy()
for key, value in detections.items()}
detections['num_detections'] = num_detections
# detection_classes should be ints.
detections['detection_classes'] = detections['detection_classes'].astype(np.int64)
image_np_with_detections = image_np.copy()
viz_utils.visualize_boxes_and_labels_on_image_array(
image_np_with_detections,
detections['detection_boxes'],
detections['detection_classes'],
detections['detection_scores'],
category_index,
use_normalized_coordinates=True,
max_boxes_to_draw=200,
min_score_thresh=.30,
agnostic_mode=False)
plt.figure()
plt.imshow(image_np_with_detections)
print('Done')
plt.show()
Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Suzuki Aerio Sedan 2007/00001.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ferrari 458 Italia Convertible 2012/00002.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Jeep Patriot SUV 2012/00003.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Toyota Camry Sedan 2012/00004.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Tesla Model S Sedan 2012/00005.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chrysler Town and Country Minivan 2012/00006.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/GMC Terrain SUV 2012/00007.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Mercedes-Benz S-Class Sedan 2012/00008.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW X5 SUV 2007/00009.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chevrolet HHR SS 2010/00010.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Buick Verano Sedan 2012/00011.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ford Freestar Minivan 2007/00012.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ford E-Series Wagon Van 2012/00013.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Hyundai Elantra Sedan 2007/00014.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Dodge Caliber Wagon 2012/00015.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Cadillac CTS-V Sedan 2012/00016.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Land Rover Range Rover SUV 2012/00017.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW X6 SUV 2012/00018.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Audi S5 Coupe 2012/00019.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/BMW X5 SUV 2007/00020.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Lamborghini Aventador Coupe 2012/00021.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Acura TL Type-S 2008/00022.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Chrysler PT Cruiser Convertible 2008/00023.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Ford Mustang Convertible 2007/00024.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Mercedes-Benz S-Class Sedan 2012/00025.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Volkswagen Golf Hatchback 1991/00026.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Nissan Juke Hatchback 2012/00027.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Volvo XC90 SUV 2007/00028.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Dodge Dakota Club Cab 2007/00029.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Acura TSX Sedan 2012/00030.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Mercedes-Benz E-Class Sedan 2012/00031.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Jeep Grand Cherokee SUV 2012/00032.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Toyota Corolla Sedan 2012/00033.jpg... Done Running inference for /content/drive/My Drive/capstone/Dataset/Car Images/Test Images/Bentley Continental Flying Spur Sedan 2007/00034.jpg...
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-80-5f4603fe6f9f> in <module>() 25 26 # input_tensor = np.expand_dims(image_np, 0) ---> 27 detections = detect_fn(input_tensor) 28 29 # All outputs are batches tensors. /usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/load.py in _call_attribute(instance, *args, **kwargs) 699 700 def _call_attribute(instance, *args, **kwargs): --> 701 return instance.__call__(*args, **kwargs) 702 703 /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/traceback_utils.py in error_handler(*args, **kwargs) 151 except Exception as e: 152 filtered_tb = _process_traceback_frames(e.__traceback__) --> 153 raise e.with_traceback(filtered_tb) from None 154 finally: 155 del filtered_tb /usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py in _convert_inputs_to_signature(inputs, input_signature, flat_input_signature) 2980 flat_input_signature, 2981 flatten_inputs)): -> 2982 raise ValueError("Python inputs incompatible with input_signature:\n" 2983 f"{format_error_message(inputs, input_signature)}.") 2984 ValueError: Python inputs incompatible with input_signature: inputs: ( tf.Tensor( [[[ 87 87 87 ... 151 151 151] [ 86 85 85 ... 152 152 152] [ 82 82 82 ... 152 152 152] ... [133 133 133 ... 133 133 133] [133 133 133 ... 133 133 133] [133 133 133 ... 133 133 133]]], shape=(1, 400, 600), dtype=uint8)) input_signature: ( TensorSpec(shape=(1, None, None, 3), dtype=tf.uint8, name='input_tensor')).
!pwd
/content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd
%cd /content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd
/content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd
%cd models
/content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd/models
!ls
my_rcnn_imagenet my_ssd_mobilenet
%cd my_ssd_mobilenet/
/content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd/models/my_ssd_mobilenet
!ls
checkpoint ckpt-24.data-00000-of-00001 ckpt-20.data-00000-of-00001 ckpt-24.index ckpt-20.index ckpt-25.data-00000-of-00001 ckpt-21.data-00000-of-00001 ckpt-25.index ckpt-21.index ckpt-26.data-00000-of-00001 ckpt-22.data-00000-of-00001 ckpt-26.index ckpt-22.index eval ckpt-23.data-00000-of-00001 pipeline.config ckpt-23.index train
%cd ..
/content/drive/MyDrive/capstone/tensorflow/workspace/training-ssd/models
%reload_ext tensorboard
%tensorboard --logdir my_ssd_mobilenet
%reload_ext tensorboard
tensorboard --logdir my_rcnn_imagenet
%cd /content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo
%cd yolov5
/content/drive/MyDrive/capstone/tensorflow/workspace/training-yolo/yolov5
%ls
CONTRIBUTING.md export.py __pycache__/ setup.cfg val.py data/ hubconf.py README.md train.py yolov5s.pt detect.py LICENSE requirements.txt tutorial.ipynb Dockerfile models/ runs/ utils/
%reload_ext tensorboard
%tensorboard --logdir runs